Comparison of our methods with various pansharpening methods on AF and AVIRIS.
\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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Hyperspectral (HS) images have found a wide range of applications in terrestrial and planetary missions. NASA is planning a HyspIRI mission [1–4] that will perform vegetation monitoring for the whole Earth. The spatial resolution of HyspIRI is 60 m. The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) [5] hyperspectral (HS) imager with 100-m resolution has been monitoring Mars surface since 2006. Although the above imagers have resolution good enough for their respective missions, many other applications such as drought monitoring, fire damage assessment, etc., require higher resolutions. Other notable applications of multispectral and hyperspectral images include target detection [6–13], anomaly and change detection [14–23], tunnel monitoring [24, 25], and Mars exploration [26, 27].
\nPansharpening of hyperspectral images usually refers to the fusion of a high-resolution (HR) panchromatic (pan) band with a low-resolution (LR) hyperspectral image cube. A generalization of the above is the fusion of high-resolution multispectral bands with low-resolution hyperspectral bands. According to Loncan et al. [28], pansharpening techniques for HS images can be classified into the following categories. The first category is the component substitution (CS) approach. Well-known CS approaches include Principal Component Analysis (PCA) [29], Gram-Schmidt (GS) [30], GS Adaptive (GSA) [31], and others. The CS approach is based on the substitution of a component with the pan image. The second category is the multiresolution analysis (MRA) approach. The MRA approach relies on the injection of spatial details that are obtained through a multiresolution decomposition of the pan image into the resampled hyperspectral bands. Some well-known algorithms in this category are Modulation Transfer Function Generalized Laplacian Pyramid (MTF-GLP) [32], MTF-GLP with High-Pass Modulation (MTF-GLP-HPM) [33], Hysure [34, 35], and Smoothing Filter-based Intensity Modulation (SFIM) [36]. The third category contains the hybrid approaches that use concepts from different classes of methods, namely from CS and MRA ones. For example, guided filter PCA (GFPCA) [37] belongs to this group. The fourth category involves Bayesian inference, which interprets the fusion process through the posterior distribution of the Bayesian fusion model. Due to the ill-posed fusion problem, the Bayesian methodology can easily regularize the problem by defining a prior distribution for the scene of interest. Exemplar algorithms include Bayesian naive [38] and Bayesian sparse [39]. The fifth category is known as non-negative matrix Factorization (NMF). Representative methods in this category include the coupled non-negative matrix factorization (CNMF) [40] method.
\nIn addition to the above five categories, we noticed that deep learning-based approaches have been investigated in recent years to address pansharpening for hyperspectral images. In Licciardi et al. [41], the authors proposed the first deep learning-based fusion method, which used autoencoder. In 2015, a modified sparse tied-weights denoising autoencoder was proposed by Huang et al. [42]. The authors assumed that there exists a mapping function between LR and HR images for both pan and MS or HS. During the training process, an LR pan was generated by the interpolated MS, then the mapping function was learned by the given LR pan patches as the input and HR pan patches as the output. In 2016, a supervised three-layer convolutional neural network was proposed by [43] to learn the mapping function between the input HR pan with the interpolated LR MS, and the output HR MS. In Qu et al. [44], a new pansharpening algorithm based on deep learning autoencoder was proposed. Preliminary pansharpening results using both hyperspectral and multispectral images are encouraging.
\nFrom the application viewpoint, we categorize the various pansharpening algorithms into three groups. Group 1 methods include coupled non-negative matrix factorization (CNMF) [40], Bayesian naive [38], and Bayesian sparse [39]. These methods require the point spread function (PSF) to be available and they perform better than other methods in most cases. Group 2 methods do not require PSF and contain Principal Component Analysis (PCA) [29], guided filter PCA (GFPCA) [37], Gram-Schmidt (GS) [30], GS Adaptive (GSA) [31], Modulation Transfer Function Generalized Laplacian Pyramid (MTF-GLP) [32], MTF-GLP with High-Pass Modulation (MTF-GLP-HPM) [33], Hysure [34, 35], and Smoothing Filter-based Intensity Modulation (SFIM) [36]. Group 3 methods use only the LR HS images and contain the super-resolution (SR) [45], the bicubic method [46], and plug-and-play alternating direction multiplier method (PAP-ADMM) [47].
\nThis chapter is organized as follows. In Section 2, we review the basic idea of color mapping and its variants. In Section 3, we include extensive experiments to illustrate the performance of various pansharpening algorithms. Finally, we conclude our chapter with some future research directions.
\nAs shown in \nFigure 1\n, the idea of color mapping is to map a color pixel c\n(i, j) at location (i, j) with R, G, B bands to a hyperspectral pixel X\n(i, j) at the same location. This mapping is represented by a transformation matrix T, that is
\nSystem flow of color mapping. LR denotes low resolution; HR denotes high resolution; LR C denotes the set of low-resolution color pixels; LR H denotes the set of low-resolution hyperspectral pixel; HR Hyper denotes high-resolution hyperspectral.
where X\n(i, j) ∈ RN\n is a single hyperspectral pixel with N spectral bands, T ∈ R\n\nN × M\n, c\n(i, j) ∈ RM\n is a color pixel with M spectral bands, and N>>M. Here, M can be just one band such as the pan band. Hence, color mapping is quite general as it encompasses pan, color, and MS images. Our goal is to generate a HR HS image given a HR color image and an LR HS image. To determine T in Eq. (1), we simulate an LR color image by down-sampling the HR color image. The LR color image and the LR HS image are then used to determine T, which is then used to generate the HR HS image pixel by pixel.
\nLet us denote H as the set of all hyperspectral pixels X\n(i, j) for all (i,j) in the image and C as the set of all color pixels c\n(i, j) for all (i,j) in the image, i =1, …, NR\n, j = 1, …, NC\n with NR\n the number of rows and NC\n the number of columns in the image. Since X\n(i, j) and c\n(i, j) are vectors, H and C can be expressed as
\nWe call the mapping (1) the global version and all pixels in C and H are used in estimating T.
\nTo estimate T, we use the least-square approach, which minimizes the error
\nSolving T in Eq. (3) yields [48]
\nTo avoid instability, we can add a regularization term in Eq. (3). That is,
\nAnd the optimal T becomes [48]
\nwhere λ is a regularization parameter and I is an identity matrix with the same dimension as CCT\n.
\nFor many hyperspectral images, the band wavelengths range from 0.4 to 2.5μm. For color images, the R, G, and B wavelengths are 0.65, 0.51, and 0.475μm, respectively. Because the three color bands may have little correlation with higher-number bands in the hyperspectral image, we found that it is beneficial to extract several higher-number bands from LR HS image and stack them with the LR color bands. This idea is illustrated in \nFigure 2\n. Details can be found in [48]. Moreover, we also noticed that by adding a white band, that is, all pixel values are 1, we can deal with atmospheric and other bias effects.
\nSystem flow of hybrid color mapping.
Using the same treatment earlier, T can be obtained by minimizing the mean square error [48]
\nwhere H is the set of hyperspectral pixels and Ch\n is the set of hybrid color pixels. All the pixels in H and Ch\n are used.
\nThe optimal T can be determined as
\nWith regularization, Eq. (8) becomes [48]
\nWe further enhance our method by applying color mapping patch by patch. As shown in \nFigure 3\n, a patch of size p × p is a sub-image in the original image. The patches can be overlapped. This idea allows spatial correlation to be exploited. Our experiments showed that the mapping will become more accurate using this local patch idea. Another advantage of using patches is to split the task into many small jobs so that parallel processing is possible.
\nLocal color mapping to further enhance the SR performance. The patches apply to LR color and LR hyperspectral images.
Based on observations in [28] and our own investigations [48], some pansharpening algorithms with the incorporation of PSF can yield better performance than those without PSF. This motivates to incorporate PSF into our HCM. Our idea can be illustrated by using \nFigure 4\n. The first component is the incorporation of a single-image super-resolution algorithm to enhance the LR hyperspectral image cube. Single-image super-resolution algorithms are well known [45, 46]. The idea is to improve the resolution of an LR image by using internal image statistics. Our proposed method enhances the LR HS bands and then fuses the results using the HCM algorithm. The second component utilizes the HCM algorithm that fuses a high-resolution color image with an enhanced hyperspectral image coming out of the first component. Recently, HCM has been applied to several applications, including enhancing Worldview-3 images [49], fusion of Landsat and MODIS images [50], pansharpening of Mastcam images [51], and fusing of THEMIS and TES [52].
\nAn outline of the proposed method. We use hybrid color mapping (HCM) to fuse low-resolution (LR) and high-resolution (HR) images. For LR images, we use a single-image super-resolution algorithm where PSF is incorporated to first enhance the resolution before feeding to the HCM.
This idea was summarized in a paper [53] that was presented in ICASSP 2017. The results are comparable to other state-of-the-art algorithms in the literature [39, 40].
\nIn [54], we propose two variants of HCM. From Eq. (5), we can treat the HCM method as an L2 regularization problem. In [54], we investigate some variants of the regularization. In particular, we would like to apply L1 and L0 norms for the regularization term in Eq. (5). We favor the following two approaches: orthogonal matching pursuit (OMP) [55] and l1-minimization via augmented Lagrangian multiplier (ALM-l1) [56], in which OMP is described as an l0-based minimization problem:
\nwhere K is the sparsity level of the matrix α (K<<M × N ), and ALM-l1 solves for the l1-minimization convex relaxation problem:
\nwhere the positive-weighting parameter λ provides the trade-off between the two terms.
\nDebayering or demosaicing refers to the reconstruction of missing pixels in the Bayer pattern [57, 58] as shown in \nFigure 5\n. A variant of the Bayer pattern, known as CFA2.0 (\nFigure 6\n), was introduced in [59]. Recently, a new approach [60] to debayering was introduced based on pansharpening ideas. A thorough comparative study was performed using benchmark datasets. It was found that the pansharpening-based debayering approach holds good promise.
\nStandard Bayer pattern.
RGBW (aka CFA2.0) pattern.
Two hyperspectral image datasets were used in our experiments. One was from the Air Force (AF) and the other one was one of the NASA AVIRIS’ images. The AF image (\nFigure 7\n) has 124 bands (461–901 nm). The AVIRIS image (\nFigure 8\n) has 213 bands (380–2500 nm). Both of them are natural scenes. The AF image size is 267×342×124 and the AVIRIS image size is 300×300×213.
\nSample band of AF data.
Sample band of AVIRIS data.
The down-sampled image was used as the low-resolution hyperspectral image that needs to be improved. We picked R, G, and B bands from the original high-resolution hyperspectral image for color mapping. The bicubic method in the following plots was implemented by up-sampling the low-resolution image using bicubic interpolation. The results of bicubic method were used as baseline for comparison study.
\nSimilar to [28], five performance metrics are included here.
\n\nTime: It is the computational time in seconds. This metric is machine dependent and varies with runs. However, it gives a relative measure of the computational complexity of different algorithms.
\n\nRoot mean-squared error (RMSE) [28]: Given two matrices X and \n
The ideal value of RMSE is 0 if there is perfect reconstruction. To show the performance for each band, we also used RMSE(λ), which is the RMSE value between X(λ) and \n
\nCross-correlation (CC) [28]: It is defined as
\nwhere mλ\n is the number of bands in the hyperspectral image and CCS is the cross-correlation for a single-band image, given by
\nThe ideal value of CC is 1. We also used CC(λ) = \n
\nSpectral Angle Mapper (SAM) [28]: It is defined as
\nwhere, for two vectors a, b ∈ Rmλ\n\n,
\n〈a, b〉 is the inner product between two vectors and ‖•‖ denotes the two norms of a vector. The ideal value of SAM is 0.
\n\nErreur relative globale adimensionnelle de synthèse (ERGAS) [28]: It is defined as
\nwhere d is the ratio between the linear resolutions of the PAN and HS images, μk\n is the sample mean of the kth band of X. The ideal value of ERGAS is 0.
\nWe first compared global color mapping (Section 2.1) with local color mapping (Section 2.2) and bicubic interpolation method. \nFigures 9\n and \n10\n show the RMSE between the ground-truth hyperspectral images and the super-resolution images produced by different methods. We can see that local color mapping is always better than global color mapping. In the AF image, for lower-number bands where R, G, and B bands reside, both global and local color mapping produce better performance than bicubic method. See \nFigure 9\n. However, for higher-number bands, bicubic method is better. The reason is that the spectral correlation between higher-number bands and R, G, B bands is weak. For the AVIRIS image, the local color mapping results shown in \nFigure 10\n are better than both global color mapping and bicubic results across almost all spectral bands.
\nRMSE comparison for AF dataset.
RMSE comparison for AVIRIS dataset.
\n\nFigures 11\n and \n12\n show the performance of hybrid color mapping as well as RGBW (R, G, B, and white bands) color mapping described in Section 2. W refers to the white band, that is, an image with all 1s. We can see that adding a white band improves the performance across all bands. Moreover, the hybrid method, which combines more bands from the bicubic interpolated higher-number bands, performed the best in all of the bands. Here, all methods used local patches. The patch size is 4 × 4 and there is no overlapping between the patches.
\nRGB color mapping versus hybrid color mapping for AF dataset. W stands for white band. All methods are local. RGBW means a white band is added to the R, G, B bands.
RGB color mapping versus hybrid color mapping for AVIRIS dataset. W stands for white band. All methods are local. RGBW means a white band is added to the R, G, B bands.
Now, we include a comparison with a state-of-the-art pan-sharpening algorithm [61] and another single-image SR algorithm [45]. Both algorithms are recent and compared favorably than their counterparts in their categories. \nFigures 13\n and \n14\n show the performance of variational wavelet pan-sharpening (VWP) [61], bicubic interpolation, single-image SR [45], and our local hybrid color mapping. We used red color band from ground-truth image as the pan image. For NASA data, we observe that in some bands, VWP is better than bicubic and single-image SR methods. However, for bands far away from the reference band, the error is large. In addition, other methods are always worse than our hybrid color mapping method. The reason that VWP [61] did not perform well in this study is perhaps due to the lack of a pan image which has a spectrum that extends to the high-wavelength regions. If the pan image extends to higher-wavelength regions, it is believed that VWP will perform well. We are grateful to the authors of [45] for sharing their source codes with us. The reason that the single-image SR [45] method did not perform well is because it was designed for color images, not for hyperspectral images.
\nHybrid color mapping versus variational wavelet pan-sharpening (VWP), bicubic, and single-image SR [45] for AF dataset. Scale factor is 3.
Hybrid color mapping versus variational wavelet pan-sharpening (VWP), bicubic, and single-image SR [45] for AF dataset. Scale factor is 3.
The goal of our research is not only to improve the visual performance of the hyperspectral images by enhancing the spatial resolution but also to enhance the pixel clustering performance as well. \nFigures 15\n–\n18\n show clustering results using end members extracted from ground-truth AVIRIS hyperspectral image. We used K-means end member extraction technique to determine the clusters. It should be emphasized that we are not performing land-cover classification, which usually requires land-cover spectral signatures as well as atmospheric compensation. The physical meaning of each cluster is not our concern. Comparing \nFigures 15\n and \n16\n, one can see that hybrid color mapping is significantly better than the bicubic method in terms of the fine details of the images. Moreover, \nFigure 16\n is a lot closer to the ground-truth image in \nFigure 17\n. The images also show that the hybrid color mapping produces much better clustering than the bicubic method as shown in \nFigure 18\n.
\nBicubic pixel classification.
Hybrid color mapping pixel classification.
Ground-truth pixel classification.
Pixel classification accuracy.
It should be noted that in all of the above studies, we have the following observations:
In the AVIRIS image, our local hybrid color mapping algorithm performed consistently well in all bands. That is, our method is better than bicubic and other methods in all bands.
In the AF image, our local hybrid color mapping algorithm performed also better than others across all bands. However, the performance in lower-number bands is better than that in higher-number bands. This is because the correlation between lower-number bands and higher-number bands is not that good.
We are curious about what will happen if bands other than R, G, B are chosen as reference image in generating the transformation T. \nFigures 19\n and \n20\n show the comparison. \nFigure 19\n compares the RMSEs using only R, G, B and the case of using bands 20, 50, and 100, for the AF image. \nFigure 20\n compares the RMSEs using only RGB and the case of using bands 20, 90, and 170, for the AVIRIS image. We can observe that if we pick bands from low-, middle-, and high-number bands assuming that they are available, then the overall performance can be much better than that of using R, G, B bands only. The observation shows that we should utilize not only R, G, B bands information but also other bands information such as infrared image and thermal image if they are available.
We investigated the impact of adding regularization to the solution of finding T. In our simulations, we used λ = 10−5\nv and v is the maximum singular value of CCT\n in Eq. (5) or ChCh\nT\n in Eq. (8). From \nFigures 21\n and \n22\n, it can be seen that regularization can also further enhance the performance of our algorithm.
We also investigated the impact of different scaling factors in the down-sampling process. As can be seen in the supplemental materials section, results indicate that bicubic method performs well in low-scaling factors of 1.5 where spatial correlation is high. However, in our applications, we are interested in airborne and satellite images where spatial correlation is weak.
Local color mapping with RGBW bands versus local color mapping with low-, middle-, and high-number bands for AF dataset.
Local color mapping with RGBW bands versus local color mapping with low-, middle-, and high-number bands for AVIRIS dataset.
Regularization improves the stability as well as the performance. Here, local hybrid color mapping has been used for AF dataset.
Regularization improves the stability as well as the performance. Here, local hybrid color mapping has been used for AVIRIS dataset.
Here, we focus on a comparison with Groups 1–3 algorithms in the literature. We also compare the performance of different variants of HCM.
\nGroup 1 methods (BN, BS, CNMF) require PSF. In general, Group 1 methods performed better than Groups 1 and 2. In Section 2.4, we also incorporated PSF into our HCM algorithm. Based on results in \nTable 1\n, we can see that the HCM (L2-norm) yielded better results than those Group 1 methods for the AF data. For the AVIRIS data, BS performed the best.
\n\n | \n | \n | \n | AF | \n\n | \n | \n | \n | AVIRIS | \n\n | \n |
---|---|---|---|---|---|---|---|---|---|---|---|
Group | \nMethods | \nTime | \nRMSE | \nCC | \nSAM | \nERGAS | \nTime | \nRMSE | \nCC | \nSAM | \nERGAS | \n
1 | \nCNMF [34] | \n12.52 | \n0.5992 | \n0.9922 | \n1.4351 | \n1.7229 | \n23.75 | \n32.2868 | \n0.9456 | \n0.9590 | \n2.1225 | \n
\n | Bayes Naive [32] | \n0.58 | \n0.4357 | \n0.9881 | \n1.2141 | \n1.6588 | \n0.86 | \n67.2879 | \n0.9474 | \n0.8136 | \n2.1078 | \n
\n | Bayes Sparse [33] | \n208.82 | \n0.4133 | \n0.9900 | \n1.2395 | \n1.5529 | \n235.50 | \n51.7010 | \n0.9619 | \n\n0.7635\n | \n1.8657 | \n
2 | \nSFIM [30] | \n0.99\n+\n\n | \n0.7132 | \n0.98489 | \n1.4936 | \n2.2087 | \n1.56y | \n62.02333 | \n0.94911 | \n0.918 | \n2.0404 | \n
\n | MTF-GLP [26] | \n1.38\n+\n\n | \n0.8177 | \n0.98314 | \n1.6095 | \n2.4541 | \n2.25y | \n55.604 | \n0.95451 | \n0.91031 | \n1.9703 | \n
\n | MTF-GLP-HTM [27] | \n1.40\n+\n\n | \n0.8050 | \n0.98356 | \n1.546 | \n2.4214 | \n2.23y | \n55.641 | \n0.95451 | \n0.90544 | \n1.9718 | \n
\n | GS [24] | \n1.05\n+\n\n | \n2.1783 | \n0.85792 | \n2.4421 | \n7.0807 | \n1.83y | \n54.8851 | \n0.95597 | \n0.93488 | \n1.9524 | \n
\n | GSA [25] | \n1.21\n+\n\n | \n0.7435 | \n0.98764 | \n1.5156 | \n2.1764 | \n1.98y | \n32.2331 | \n\n0.97016\n | \n0.85211 | \n\n1.6525\n | \n
\n | PCA [23] | \n2.37\n+\n\n | \n2.3816 | \n0.83824 | \n2.6355 | \n7.7176 | \n2.98y | \n48.9125 | \n0.96087 | \n0.9173 | \n1.8605 | \n
\n | GFPCA [31] | \n1.17\n+\n\n | \n0.6482 | \n0.98615 | \n1.5382 | \n2.0607 | \n2.17y | \n62.5283 | \n0.93858 | \n1.1736 | \n2.2566 | \n
\n | Hysure [28, 29] | \n117.06\n+\n\n | \n0.8717 | \n0.98059 | \n1.7882 | \n2.6294 | \n62.47y | \n38.3131 | \n0.9598 | \n1.0181 | \n1.8586 | \n
3 | \nPAP-ADMM [42] | \n2144.00 | \n0.4408 | \n0.98849 | \n1.1657 | \n1.6476 | \n3368.00 | \n66.2481 | \n0.9531 | \n0.7848 | \n1.9783 | \n
\n | Super-Resolution [39] | \n279.18 | \n0.5232 | \n0.9839 | \n1.3215 | \n1.9584 | \n1329.59 | \n86.7154 | \n0.9263 | \n0.9970 | \n2.4110 | \n
\n | Bicubic [40] | \n\n0.04\n | \n0.5852 | \n0.9807 | \n1.3554 | \n2.1560 | \n\n0.10\n | \n92.2143 | \n0.9118 | \n1.0369 | \n2.5728 | \n
Ours | \nL2 Norm | \n3.70\n+\n\n | \n\n0.3889\n | \n\n0.9965\n | \n\n1.1928\n | \n\n1.0986\n | \n4.47 | \n\n32.2094\n | \n0.9553 | \n0.9453 | \n1.9101 | \n
\n | L0 Norm | \n33.36\n+\n\n | \n0.4288 | \n0.9958 | \n1.3677 | \n1.2095 | \n81.11 | \n32.1875 | \n0.9552 | \n0.9362 | \n1.9109 | \n
\n | L1 Norm | \n1293.94\n+\n\n | \n0.3917 | \n0.9965 | \n1.2259 | \n1.1064 | \n6.22 | \n32.1875 | \n0.9552 | \n0.9362 | \n1.9109 | \n
Comparison of our methods with various pansharpening methods on AF and AVIRIS.
These methods involve PAP-ADMM but we did not include PAP-ADMM’s runtime in order to illustrate the differences.
Bold numbers indicate results from the best algorithms.
Only the GSA method in Group 2 performed better than others for the AVIRIS. Other methods in Group 2 did not perform well as compared to HCM and Group 2 methods. However, Group 2 methods are generally more computationally efficient, which may be advantageous in some real-time applications.
\nGroup 3 methods do not require PSF or high-resolution color images. Consequently, the performance is generally poor as compared to others. This is understandable.
\nFrom \nTable 1\n, we can see that the L2 version of HCM performed better than other variants. This can also be seen from \nFigures 23\n–\n26\n. However, one key advantage of the L1 and L0 variants is that the models are simpler, as the coefficients of L0 are clustered and the coefficients of L1 are much fewer than that of L2. The above can be confirmed by inspecting \nFigures 27\n and \n28\n. We would like to mention that another advantage of the sparsity formulation is that it can handle noisy measurements in the hyperspectral images [54].
\nRMSE comparison of the three variants of HCM (L2, L1, and L0) for the AF dataset.
CC comparison of the three variants of HCM (L2, L1, and L0) for the AF dataset.
RMSE comparison of the three variants of HCM (L2, L1, and L0) for the AVIRIS dataset.
CC comparison of the three variants of HCM (L2, L1, and L0) for the AVIRIS dataset.
Coefficients in the model of the HCM variants for the AF dataset.
Coefficients in the model of the HCM variants for the AVIRIS dataset.
In this chapter, we review the various pansharpening algorithms for hyperspectral images. We then focus on one recent algorithm known as hybrid color mapping. Several variants are described. The performance of HCM is thoroughly compared with other methods in the literature.
\nOne future direction is to investigate the performance of different pansharpening algorithms in the presence of noise. Another direction is to apply the pansharpened images to different applications such as target detection [11–13], border monitoring [24, 25], and anomaly detection [14–22, 62].
\nStress corrosion cracking (SCC) in chemical, petrochemical, and power plant industries is an insidious form of corrosion, which causes a lot of financial losses and human damages [1, 2, 3, 4, 5]. This phenomenon is associated with a combination of tensile stress, environment, and some metallurgical conditions as described in Figure 1.
The essential requirements for SCC.
During stress corrosion cracking, the metal or alloy is virtually unattacked over most of its surface, while fine and branch cracks progress through the bulk of material [6]. It is shown in Figure 2. This cracking phenomenon has serious consequences since it can occur under stresses much lower than design stresses and lead the equipment and structures to premature failures [7, 8, 9, 10, 11].
Crack development in carbon steel exposed to nitrate solution.
Stress corrosion cracking starts from corrosion sites at the material surfaces and progresses into a brittle manner. The process of cracking is not strictly a mechanical process, as the corrosivity of the environment strongly affects the fracture mode. Both intergranular and transgranular stress corrosion cracking are observed. Intergranular cracking proceeds along grain boundaries, while transgranular cracking advances without apparent preference for boundaries [12]. An example of stress corrosion cracking in which the crack has progressed in both intergranular and transgranular paths is shown in Figure 3. The development mode of cracking depends on the composition and microstructure of the material and environment.
Intergranular and transgranular stress corrosion cracking of the AISI 316L stainless steel at polythionic acid environment [8].
In this chapter, the conditions for the occurrence of SCC are first introduced. Then, the stress corrosion cracking mechanism for various materials in conditions that are susceptible is discussed in detail. The design of industrial structures and components is usually based on tensile properties, which have many disadvantages. So, the science of fracture mechanics applies in the situations prone to SCC because of the inevitability of manufacturing and service defects in materials and for considering the role of such imperfections. Methods of prevention based on corrosion science and empirical data are presented. Finally, practical examples are given to better understand the issue.
Not all metal-environment combinations are susceptible to cracking. In other words, the environment for occurrences of SCC for each metal or alloys is specific. Also, the resources of stress for each case of failure may be different.
Austenitic stainless steels suffer from SCC in chlorides, caustic, and polythionic acid. When austenitic stainless steels with sufficient carbon content (more than 0.03 wt.%) are heated in the range of 415–850°C, their microstructure becomes susceptible to precipitation of chromium carbides (M23C6) along grain boundaries known as sensitization [9, 12, 13]. Formation of Cr-rich carbides along grain boundaries may drastically deplete free chromium content in the area adjacent to the grain boundaries and render them susceptible to rapid preferential dissolution. Sensitized steels are most susceptible; the stress corrosion cracking of nonsensitized steels is also observed [14, 15]. Dissolution of grain boundaries in some corrosive environments aside from tensile stress led these types of materials to SCC.
Seasonal cracking of brass in the rainy season in an ammoniacal environment is another classical example of SCC. This was first identified on the brass cartridge used by the British Army in India. Since it is usually identified during the rainy season, it is also called seasonal cracking [12]. Alpha brass is an alloy of Cu-Zn. It can crack either intergranularly or transgranularly in nontarnishing ammonia solutions, depending on its zinc content [16, 17, 18]. Transgranular stress corrosion cracking, TGSCC, is observed in alloys with 20 or 30% Zn but not in alloys with 0.5 or 10% Zn [19, 20]. Stress corrosion cracking of Cu-Zn and Cu-A1 alloys in cuprous ammonia solutions can only occur when the parting limits for dealloying are exceeded. The parting limits are about 14 and 18 a/o for Cu-A1 and Cu-Zn, respectively [21]. Cu-A1 and Cu-Ga alloys have shown similar behaviors [19, 22].
Aluminum and all its alloys can fail by cracking along grain boundaries when simultaneously exposed to specific environments and stresses of sufficient magnitude [23, 24]. Of eight series of aluminum alloys, 2xxx, 5xxx, and 7xxx aluminum alloys are susceptible to SCC. Among them, 7xxx series aluminum alloys have a specific application in aerospace, military, and structural industries due to superior mechanical properties. In these high-strength 7xxx aluminum alloys, SCC plays a vital factor of consideration, as these failures are catastrophic during the service [25].
Carbon and low alloy steels have shown SCC in a wide range of environments that tend to form a protective passive or oxide film [26, 27, 28, 29, 30]. The environments that would passivate carbon steels have been found to cause SCC, including strong caustic solutions, phosphates, nitrates, carbonates, ethanol, and high-temperature water. The problems are important for both economic and safety reasons, due to the extensive use of carbon steels [31]. For example, nitrate cracking in an ammonium nitrate plant caused by catastrophic failures and a lot of financial losses. Caustic cracking of steam-generating boilers made of low alloy steels was a serious problem, which led an ammonia plant to repeated emergency shutdowns.
Stress corrosion cracking may be a problem whenever certain high-strength titanium alloys are exposed to aqueous and certain solvent environments [32, 33, 34, 35, 36]. For the first time, SCC of titanium was reported by Kiefer and Harple who describe the cracking phenomena with commercially pure titanium in red fuming nitric acid [37]. Hot salt cracking of titanium alloys was reported in turbine blades that operate at high temperature in the mid-1950s. The subject became very active in the early 1960s because of the SCC problem connected to these alloys in a transportation program [38].The first known report of stress corrosion cracking of titanium alloys in room temperature aqueous environments was that of Brown. He found that titanium alloys, 8% aluminum–1% molybdenum–1% vanadium alloy (Ti, 8–1–1), were susceptible to SCC in seawater [38].
Another requirement for SCC to occur is a corrosive environment. The environments for SCC are specific because not all environments promote SCC. For those alloys that develop a protective film, an aggressive ion is required to promote SCC. The aggressive media to passive layer of stainless steels are chlorides, caustic, and polythionic acid. The austenitic stainless steel series 300 is more susceptible in an environment containing chlorides. Chlorides will not cause SCC unless an aqueous phase is present. It appears that stress corrosion cracking in austenitic stainless steels in the presence of chlorides proceeds transgranularly and usually occurs at temperature above 70°C
Metal | Environment |
---|---|
Al alloys | NaCl-H2O2 solutions |
NaCl solutions | |
Seawater | |
Copper alloys | Ammonia vapor and solutions |
Amines | |
Water or water vapor | |
Gold alloys | FeCl3 solutions |
Acetic acid-salt solutions | |
Inconel | Caustic soda solutions |
Lead | Lead acetate solutions |
Magnesium alloys | NaCl-Na2CrO4 solutions |
Rural and coastal atmospheres | |
Seawater | |
Distilled water | |
Nickel | Fused caustic soda |
Steels | NaOH solutions |
NaOH-Na2SiO4 solutions | |
Calcium, ammonium, and sodium nitrite solutions | |
Mixed acids (H2SO4-HNO3) | |
Acidic H2S solutions | |
Seawater | |
Carbonate-bicarbonate solutions | |
Stainless steels | Acidic chloride solutions |
NaCl-H2O2 solutions | |
Seawater | |
H2S | |
NaOH-H2S solutions | |
Condensing steam from chloride waters | |
Titanium alloys | Red fuming nitric acid |
Seawater | |
Methanol-HCl |
The stress in the form of tensile (not compressive) plays a key role in the SCC fracture processes. In fact, SCC would never have occurred in the absence of stress. The required tensile stresses may be in the form of directly applied stresses, thermal, in the form of residual stresses, or a combination of all [8, 50]:
For SCC to occur alone by applied stress, it must have a very high magnitude. The welding and mechanical residual stresses are the main sources of stress attributed to the stress corrosion cracking. The welding residual stress is produced as a result of nonuniform temperature changes during welding operation and can be calculated from thermal strain vectors.
The thermal strain vector,
in which
The operational thermal stress can also be calculated from Eq. 2. Mechanical workings such as cold deformation and forming, machining, and grinding are the other sources, which introduce residual stresses [8, 51].
Extensive investigations have been devoted to find mechanisms of SCC for different materials and environments. An SCC failure illustrates the combined effects of mechanical, physical, and chemical/electrochemical factors causing the separation of metal bonds at the crack tip, thereby advancing the crack. Three mechanisms for SCC have been proposed through the investigations [52]:
This model supposes that there are pre-existing paths in an alloy that is susceptible to anodic dissolution. Because of precipitation or solute segregation of impurities like sulfur, phosphorus, and chromium carbides, the electrochemical properties of the matrix and segregates are changed. The area adjacent to the grain boundaries is depleted from one or more alloying elements, and so under such conditions, localized galvanic cells are created (Figure 4). Since precipitation or segregation is generally anodic to the matrix of the grains, dissolution under an anodic reaction occurs and provides active path for localized corrosions [53]. Also, the removal of the protective film at the pre-existing crack tips by plastic deformation would facilitate the onset of localized corrosion.
Galvanic cell mechanism [52].
This mechanism has been extensively studied in stress corrosion cracking of alpha brass in ammoniacal environment and also proposed for caustic cracking of boiler steel. The model is based on the idea of a strain-induced rupture of the protective film, and so plastic strains play a main role in failure processes [52, 55]. The theory assumes the existence of a passivation film on a metal surface. The passivation film protects the underlying metal against corrosive agents. The passivation film is ruptured by plastic strain due to mechanical workings. After the film is ruptured, the bare metal is exposed to the corrosive environment. The processes of disruptive strain (disruption of protective film) and film formation (due to repassivation) have occurred and alternate with each other. The crack propagates when the rate of rupture of oxide film is higher than the rate of repassivation of the film [52]. The mechanism is shown in Figure 5.
Strain-generated active path mechanisms. (A) Film rupture model and (B) slip-step dissolution model [52].
This model is based on the effects of environmental species on interatomic bond strength. The theoretical fracture stress required to separate two layers of atoms of spacing b is given by [56].
where E is the Young modulus, γ is the surface energy, and b is the spacing between atoms.
This theory implies that if surface energy is reduced, then
The design of steel structure and component based on tensile properties has many disadvantages that do not take into account the role of imperfections. Fracture mechanic introduces another material characteristic, namely, fracture toughness, KIC, which considered the role of cracks and imperfection in the form of cracks in designs. In its simplest form [57].
where
According to this equation, fracture occurs when stress intensity factor,
Effects of corrosive environment on fracture toughness [12].
Since the exact mechanism of SCC has not been completely understood, prevention methods are either general or empirical in nature. Appropriate strategy should be done in order to minimize this problem to ensure not only the safety of human life but also the safety of cost. The following general methods are recommended to overcome the SCC problems [12, 52, 58, 59]:
Lowering the tensile stress in the welded component using post weld heat treatment. The post weld heat treatment reduced or eliminated residual stress on surface and through the bulk of material. Plan and low alloy steels may be a stress relief at 1100–1200°F. The range of residual stress relief temperature for austenitic stainless steels is from 1500 to 1700°F. Reduction of tensile stresses by shot peening is also recommended. Shot peening introduces surface compressive stresses.
Eliminating aggressive agents from the environment by, for example, degasification, demineralization, or distillation.
Changing the alloy is one possible solution if neither the environment nor stress can be changed. For example, it is a common practice to use Inconel (raising the nickel content) when typ. 304 stainless steel is not satisfactory.
Applying cathodic protection: impressed current cathodic protection system has been successfully used to prevent SCC of steels.
Adding inhibitors to the system if feasible: high concentrations of phosphate have been successfully used.
Coatings are sometimes used, and they depend on keeping the environment away from the metal.
After only 3 years’ service of a circulation water heater (heat exchanger), it has been shown to sever leakage and has led a methanol plant to emergency shutdown. An on-site investigation revealed extensive cracking initiated at weld area and through the tube sheet holes as it is shown in Figure 7.
Failed area (a) cracks extending in the weld joint of tube sheet to plugs and (b) branched cracks in the surface of the tube sheet and through the holes [8].
The circulation water heater is a vertical U-type heat exchanger made of austenitic stainless steels. The equipment used to decline reforms gas temperature in a methanol plant. The hot reformed gas at approximately 385°C entered the tubes and is cooled down to 168°C by exchanging the heat with processed water in the shell. The gases that flow through the tubes are mainly CO2, CO, H2, CH4, and N2 and at a pressure of 3.9 MPa. At the shell cooling process, water flows with about 6 MPa pressure.
Deposits had formed on top of the tube sheet due to shutdown errors. AISI 316L materials overheated in service because of the insulation role of the deposits. Material sensitization occurs since overheating. The presence of sulfur in the process gas aside from moisture formed polythionic acid during shutdowns. Residual stress produced by heavy machining and welding aside from operational thermal stress provided tensile stress, which is needed for SCC. Stress corrosion cracking is induced by polythionic acid. Concentrated water with other aggressive agents such as caustic and chlorides leaked through the cracks aid the failures.
Cleaning of the shell by demineralized water after each shut down in order to prevent the forming of insulating deposits above the tube sheet
Reduction of sulfur in feeding gas
Reduction of caustic and chlorides in processed water
Carryover of caustic soda (NaOH) in the steam path caused catastrophic failure of superheater stainless steel tubes in a gas-fired heater and led to an unexpected shutdown after just 5 months of continuous service following the start of production. The failure areas are shown in Figure 8. Three types of cracks are identified in various regions of the tube: circumferential cracks adjacent to the seam weld, circumferential cracks at the ribbon of the seam weld, and longitudinal cracks on the U-bend. The path of cracks was complex on the surface or in the bulk metal; all had nucleated from inside the tubes. A visual inspection revealed a white deposit, high in sodium, around the cracks on the surface of the tubes.
(a and b) Circumferential cracks adjacent to the seam weld, (c and d) circumferential cracks at the ribbon of seam weld, and (e) longitudinal cracks on the U-bend [9].
The superheater tube material was made of AISI 304H austenitic stainless steel material.
The gas-fired steam heater (FH) generates high-pressure (HP) steam for turbines for the processing of methanol. Demineralized water for the boiler and subsequent steam path is prepared in the water treatment unit. Caustic soda is injected to demineralized water for pH control. The water is transferred to the preheat exchangers, is converted to saturated high-pressure steam at 325°C and 119 MPa, and is sent to the FH. Through the FH tubes, saturated steam converted to supersaturate steam at a temperature of 505°C and pressure of 119 MPa.
The main cause of crack initiation was the increase of pH due to the rise of caustic concentration in condensed drops. Sensitized austenite grains caused by chromium carbide depletion adjacent to the grain boundaries were attacked by concentrated caustic in the HAZ metal and U-bend area and led the heater to the caustic SCC failure.
Using A335 Grade P9, a low alloy steel tube shows higher resistance to SCC than AISI 304H stainless steel
Proper discharge of the tubes during shutdowns to prevent the formation of the concentrated deposits of caustic through the tubes
After a general overhaul of a thermal power plant in Serbia in November 2014, failure of hundreds of brass condenser tubes occurred during the hydrostatic test. Also, it was noted that some backing plates had fallen off from the tubes before this test. Fracture is observed only in condenser tubes of brass, as can be seen in Figure 9.
Failure of brass condenser tubes near joining location with backing plate.
The failed tube material of the condenser was made of brass CuZn28Sn1 (admiralty brass). The cooling water (roughly filtered river water) flows through the tubes, while the hot steam flows around the tubes.
Analysis of fracture surfaces using scanning electron microscopy (SEM) has shown the brittle transgranular fracture due to the occurrence of SCC. The condenser tubes are made of brass CuZn28Sn1. Ammonia and other nitrogen compounds in the cooling water through the tubes were found. These compounds are specific agents that cause stress corrosion cracking (SCC) in brass. In the joining region of condenser tubes to backing plates, there are residual tensile stresses. During the floods in May 2014, there was an increase in the concentration of ammonia and other nitrogen compounds in the river cooling water flowing through the condenser tubes. Failure of brass condenser tubes occurred due to SCC, because the necessary conditions for the SCC occurrence were fulfilled.
The risk of SCC in brass condenser tubes can be reduced if specific substances responsible for SCC occurrence are removed, as much as possible. This can be achieved by cleaning and drying the tubes immediately after the operation delay of the power plant.
Another way to reduce the risk of SCC occurrence in condenser tubes is the replacement of existing tubes (made of brass CuZn28Sn1, very susceptible to SCC) with tubes made of alloys of greater resistance to SCC, such as copper-nickel alloys or Bi-brass alloys [61].
Stress corrosion cracking is one of the main causes of unforeseen and dangerous destruction of industrial plants. The sensitized material, certain environments, and stress are three factors necessary for the occurrence of these types of failures. The environment prone to the cracking for each metal or alloy is specific because not all environments promote the SCC. Austenitic stainless steels suffer from SCC in chlorides, caustic, and polythionic acid. Copper alloys corrode in ammonia-containing environments. Well-known specific environments for the stress corrosion cracking in Al alloys include water vapor, aqueous solutions, organic liquids, and liquid metals. The SCC of Ti alloys in aqueous chloride and methanolic chloride environments has been widely reported. The tensile stress plays a key role in the stress corrosion cracking phenomenon. The required tensile stresses may be in the form of directly applied stresses, thermal, in the form of residual stresses, or a combination of all.
If one of these three components does not exist, this type of corrosion will not occur. Therefore, the solving methods should be based on the elimination of one of these three factors. Corrosive environment modification, the stress in the form of compression, and using proper material are three general proposed methods of prevention.
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She performed research in perioperative autotransfusion and obtained the degree of PhD in 1993 publishing Peri-operative autotransfusion by means of a blood cell separator.\nBlood transfusion had her special interest being the president of the Haemovigilance Chamber TRIP and performing several tasks in local and national blood bank and anticoagulant-blood transfusion guidelines committees. Currently, she is working as an associate professor and up till recently was the dean at the Albert Schweitzer Hospital Dordrecht. She performed (inter)national tasks as vice-president of the Concilium Anaesthesia and related committees. \nShe performed research in several fields, with over 100 publications in (inter)national journals and numerous papers on scientific conferences. \nShe received several awards and is a member of Honour of the Dutch Society of Anaesthesia.",institutionString:null,institution:{name:"Albert Schweitzer Hospital",country:{name:"Gabon"}}},{id:"83089",title:"Prof.",name:"Aaron",middleName:null,surname:"Ojule",slug:"aaron-ojule",fullName:"Aaron Ojule",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Port Harcourt",country:{name:"Nigeria"}}},{id:"295748",title:"Mr.",name:"Abayomi",middleName:null,surname:"Modupe",slug:"abayomi-modupe",fullName:"Abayomi Modupe",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/no_image.jpg",biography:null,institutionString:null,institution:{name:"Landmark University",country:{name:"Nigeria"}}},{id:"94191",title:"Prof.",name:"Abbas",middleName:null,surname:"Moustafa",slug:"abbas-moustafa",fullName:"Abbas Moustafa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94191/images/96_n.jpg",biography:"Prof. Moustafa got his doctoral degree in earthquake engineering and structural safety from Indian Institute of Science in 2002. He is currently an associate professor at Department of Civil Engineering, Minia University, Egypt and the chairman of Department of Civil Engineering, High Institute of Engineering and Technology, Giza, Egypt. He is also a consultant engineer and head of structural group at Hamza Associates, Giza, Egypt. Dr. Moustafa was a senior research associate at Vanderbilt University and a JSPS fellow at Kyoto and Nagasaki Universities. He has more than 40 research papers published in international journals and conferences. He acts as an editorial board member and a reviewer for several regional and international journals. His research interest includes earthquake engineering, seismic design, nonlinear dynamics, random vibration, structural reliability, structural health monitoring and uncertainty modeling.",institutionString:null,institution:{name:"Minia University",country:{name:"Egypt"}}},{id:"84562",title:"Dr.",name:"Abbyssinia",middleName:null,surname:"Mushunje",slug:"abbyssinia-mushunje",fullName:"Abbyssinia Mushunje",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Fort Hare",country:{name:"South Africa"}}},{id:"202206",title:"Associate Prof.",name:"Abd Elmoniem",middleName:"Ahmed",surname:"Elzain",slug:"abd-elmoniem-elzain",fullName:"Abd Elmoniem Elzain",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Kassala University",country:{name:"Sudan"}}},{id:"98127",title:"Dr.",name:"Abdallah",middleName:null,surname:"Handoura",slug:"abdallah-handoura",fullName:"Abdallah Handoura",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"École Supérieure des Télécommunications",country:{name:"Morocco"}}},{id:"91404",title:"Prof.",name:"Abdecharif",middleName:null,surname:"Boumaza",slug:"abdecharif-boumaza",fullName:"Abdecharif Boumaza",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Abbès Laghrour University of Khenchela",country:{name:"Algeria"}}},{id:"105795",title:"Prof.",name:"Abdel Ghani",middleName:null,surname:"Aissaoui",slug:"abdel-ghani-aissaoui",fullName:"Abdel Ghani Aissaoui",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/105795/images/system/105795.jpeg",biography:"Abdel Ghani AISSAOUI is a Full Professor of electrical engineering at University of Bechar (ALGERIA). He was born in 1969 in Naama, Algeria. He received his BS degree in 1993, the MS degree in 1997, the PhD degree in 2007 from the Electrical Engineering Institute of Djilali Liabes University of Sidi Bel Abbes (ALGERIA). He is an active member of IRECOM (Interaction Réseaux Electriques - COnvertisseurs Machines) Laboratory and IEEE senior member. He is an editor member for many international journals (IJET, RSE, MER, IJECE, etc.), he serves as a reviewer in international journals (IJAC, ECPS, COMPEL, etc.). He serves as member in technical committee (TPC) and reviewer in international conferences (CHUSER 2011, SHUSER 2012, PECON 2012, SAI 2013, SCSE2013, SDM2014, SEB2014, PEMC2014, PEAM2014, SEB (2014, 2015), ICRERA (2015, 2016, 2017, 2018,-2019), etc.). His current research interest includes power electronics, control of electrical machines, artificial intelligence and Renewable energies.",institutionString:"University of Béchar",institution:{name:"University of Béchar",country:{name:"Algeria"}}},{id:"99749",title:"Dr.",name:"Abdel Hafid",middleName:null,surname:"Essadki",slug:"abdel-hafid-essadki",fullName:"Abdel Hafid Essadki",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"École Nationale Supérieure de Technologie",country:{name:"Algeria"}}},{id:"101208",title:"Prof.",name:"Abdel Karim",middleName:"Mohamad",surname:"El Hemaly",slug:"abdel-karim-el-hemaly",fullName:"Abdel Karim El Hemaly",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/101208/images/733_n.jpg",biography:"OBGYN.net Editorial Advisor Urogynecology.\nAbdel Karim M. A. El-Hemaly, MRCOG, FRCS � Egypt.\n \nAbdel Karim M. A. El-Hemaly\nProfessor OB/GYN & Urogynecology\nFaculty of medicine, Al-Azhar University \nPersonal Information: \nMarried with two children\nWife: Professor Laila A. Moussa MD.\nSons: Mohamad A. M. El-Hemaly Jr. MD. Died March 25-2007\nMostafa A. M. El-Hemaly, Computer Scientist working at Microsoft Seatle, USA. \nQualifications: \n1.\tM.B.-Bch Cairo Univ. June 1963. \n2.\tDiploma Ob./Gyn. Cairo Univ. April 1966. \n3.\tDiploma Surgery Cairo Univ. Oct. 1966. \n4.\tMRCOG London Feb. 1975. \n5.\tF.R.C.S. Glasgow June 1976. \n6.\tPopulation Study Johns Hopkins 1981. \n7.\tGyn. Oncology Johns Hopkins 1983. \n8.\tAdvanced Laparoscopic Surgery, with Prof. Paulson, Alexandria, Virginia USA 1993. \nSocieties & Associations: \n1.\t Member of the Royal College of Ob./Gyn. London. \n2.\tFellow of the Royal College of Surgeons Glasgow UK. \n3.\tMember of the advisory board on urogyn. FIGO. \n4.\tMember of the New York Academy of Sciences. \n5.\tMember of the American Association for the Advancement of Science. \n6.\tFeatured in �Who is Who in the World� from the 16th edition to the 20th edition. \n7.\tFeatured in �Who is Who in Science and Engineering� in the 7th edition. \n8.\tMember of the Egyptian Fertility & Sterility Society. \n9.\tMember of the Egyptian Society of Ob./Gyn. \n10.\tMember of the Egyptian Society of Urogyn. \n\nScientific Publications & Communications:\n1- Abdel Karim M. El Hemaly*, Ibrahim M. Kandil, Asim Kurjak, Ahmad G. Serour, Laila A. S. Mousa, Amr M. Zaied, Khalid Z. El Sheikha. \nImaging the Internal Urethral Sphincter and the Vagina in Normal Women and Women Suffering from Stress Urinary Incontinence and Vaginal Prolapse. Gynaecologia Et Perinatologia, Vol18, No 4; 169-286 October-December 2009.\n2- Abdel Karim M. El Hemaly*, Laila A. S. Mousa Ibrahim M. Kandil, Fatma S. El Sokkary, Ahmad G. Serour, Hossam Hussein.\nFecal Incontinence, A Novel Concept: The Role of the internal Anal sphincter (IAS) in defecation and fecal incontinence. Gynaecologia Et Perinatologia, Vol19, No 2; 79-85 April -June 2010.\n3- Abdel Karim M. El Hemaly*, Laila A. S. Mousa Ibrahim M. Kandil, Fatma S. El Sokkary, Ahmad G. Serour, Hossam Hussein.\nSurgical Treatment of Stress Urinary Incontinence, Fecal Incontinence and Vaginal Prolapse By A Novel Operation \n"Urethro-Ano-Vaginoplasty"\n Gynaecologia Et Perinatologia, Vol19, No 3; 129-188 July-September 2010.\n4- Abdel Karim M. El Hemaly*, Ibrahim M. Kandil, Laila A. S. Mousa and Mohamad A.K.M.El Hemaly.\nUrethro-vaginoplasty, an innovated operation for the treatment of: Stress Urinary Incontinence (SUI), Detursor Overactivity (DO), Mixed Urinary Incontinence and Anterior Vaginal Wall Descent. \nhttp://www.obgyn.net/urogyn/urogyn.asp?page=/urogyn/articles/ urethro-vaginoplasty_01\n\n5- Abdel Karim M. El Hemaly, Ibrahim M Kandil, Mohamed M. Radwan.\n Urethro-raphy a new technique for surgical management of Stress Urinary Incontinence.\nhttp://www.obgyn.net/urogyn/urogyn.asp?page=/urogyn/articles/\nnew-tech-urethro\n\n6- Abdel Karim M. El Hemaly, Ibrahim M Kandil, Mohamad A. Rizk, Nabil Abdel Maksoud H., Mohamad M. Radwan, Khalid Z. El Shieka, Mohamad A. K. M. El Hemaly, and Ahmad T. El Saban.\nUrethro-raphy The New Operation for the treatment of stress urinary incontinence, SUI, detrusor instability, DI, and mixed-type of urinary incontinence; short and long term results. \nhttp://www.obgyn.net/urogyn/urogyn.asp?page=urogyn/articles/\nurethroraphy-09280\n\n7-Abdel Karim M. El Hemaly, Ibrahim M Kandil, and Bahaa E. El Mohamady. Menopause, and Voiding troubles. \nhttp://www.obgyn.net/displayppt.asp?page=/English/pubs/features/presentations/El-Hemaly03/el-hemaly03-ss\n\n8-El Hemaly AKMA, Mousa L.A. Micturition and Urinary\tContinence. Int J Gynecol Obstet 1996; 42: 291-2. \n\n9-Abdel Karim M. El Hemaly.\n Urinary incontinence in gynecology, a review article.\nhttp://www.obgyn.net/urogyn/urogyn.asp?page=/urogyn/articles/abs-urinary_incotinence_gyn_ehemaly \n\n10-El Hemaly AKMA. Nocturnal Enuresis: Pathogenesis and Treatment. \nInt Urogynecol J Pelvic Floor Dysfunct 1998;9: 129-31.\n \n11-El Hemaly AKMA, Mousa L.A.E. Stress Urinary Incontinence, a New Concept. Eur J Obstet Gynecol Reprod Biol 1996; 68: 129-35. \n\n12- El Hemaly AKMA, Kandil I. M. Stress Urinary Incontinence SUI facts and fiction. Is SUI a puzzle?! http://www.obgyn.net/displayppt.asp?page=/English/pubs/features/presentations/El-Hemaly/el-hemaly-ss\n\n13-Abdel Karim El Hemaly, Nabil Abdel Maksoud, Laila A. Mousa, Ibrahim M. Kandil, Asem Anwar, M.A.K El Hemaly and Bahaa E. El Mohamady. \nEvidence based Facts on the Pathogenesis and Management of SUI. http://www.obgyn.net/displayppt.asp?page=/English/pubs/features/presentations/El-Hemaly02/el-hemaly02-ss\n\n14- Abdel Karim M. El Hemaly*, Ibrahim M. Kandil, Mohamad A. Rizk and Mohamad A.K.M.El Hemaly.\n Urethro-plasty, a Novel Operation based on a New Concept, for the Treatment of Stress Urinary Incontinence, S.U.I., Detrusor Instability, D.I., and Mixed-type of Urinary Incontinence.\nhttp://www.obgyn.net/urogyn/urogyn.asp?page=/urogyn/articles/urethro-plasty_01\n\n15-Ibrahim M. Kandil, Abdel Karim M. El Hemaly, Mohamad M. Radwan: Ultrasonic Assessment of the Internal Urethral Sphincter in Stress Urinary Incontinence. The Internet Journal of Gynecology and Obstetrics. 2003. Volume 2 Number 1. \n\n\n16-Abdel Karim M. El Hemaly. Nocturnal Enureses: A Novel Concept on its pathogenesis and Treatment.\nhttp://www.obgyn.net/urogynecolgy/?page=articles/nocturnal_enuresis\n\n17- Abdel Karim M. El Hemaly. Nocturnal Enureses: An Update on the pathogenesis and Treatment.\nhttp://www.obgyn.net/urogynecology/?page=/ENHLIDH/PUBD/FEATURES/\nPresentations/ Nocturnal_Enuresis/nocturnal_enuresis\n\n18-Maternal Mortality in Egypt, a cry for help and attention. The Second International Conference of the African Society of Organization & Gestosis, 1998, 3rd Annual International Conference of Ob/Gyn Department � Sohag Faculty of Medicine University. Feb. 11-13. Luxor, Egypt. \n19-Postmenopausal Osteprosis. The 2nd annual conference of Health Insurance Organization on Family Planning and its role in primary health care. Zagaziz, Egypt, February 26-27, 1997, Center of Complementary Services for Maternity and childhood care. \n20-Laparoscopic Assisted vaginal hysterectomy. 10th International Annual Congress Modern Trends in Reproductive Techniques 23-24 March 1995. Alexandria, Egypt. \n21-Immunological Studies in Pre-eclamptic Toxaemia. Proceedings of 10th Annual Ain Shams Medical Congress. Cairo, Egypt, March 6-10, 1987. \n22-Socio-demographic factorse affecting acceptability of the long-acting contraceptive injections in a rural Egyptian community. Journal of Biosocial Science 29:305, 1987. \n23-Plasma fibronectin levels hypertension during pregnancy. The Journal of the Egypt. Soc. of Ob./Gyn. 13:1, 17-21, Jan. 1987. \n24-Effect of smoking on pregnancy. Journal of Egypt. Soc. of Ob./Gyn. 12:3, 111-121, Sept 1986. \n25-Socio-demographic aspects of nausea and vomiting in early pregnancy. Journal of the Egypt. Soc. of Ob./Gyn. 12:3, 35-42, Sept. 1986. \n26-Effect of intrapartum oxygen inhalation on maternofetal blood gases and pH. Journal of the Egypt. Soc. of Ob./Gyn. 12:3, 57-64, Sept. 1986. \n27-The effect of severe pre-eclampsia on serum transaminases. The Egypt. J. Med. Sci. 7(2): 479-485, 1986. \n28-A study of placental immunoreceptors in pre-eclampsia. The Egypt. J. Med. Sci. 7(2): 211-216, 1986. \n29-Serum human placental lactogen (hpl) in normal, toxaemic and diabetic pregnant women, during pregnancy and its relation to the outcome of pregnancy. Journal of the Egypt. Soc. of Ob./Gyn. 12:2, 11-23, May 1986. \n30-Pregnancy specific B1 Glycoprotein and free estriol in the serum of normal, toxaemic and diabetic pregnant women during pregnancy and after delivery. Journal of the Egypt. Soc. of Ob./Gyn. 12:1, 63-70, Jan. 1986. Also was accepted and presented at Xith World Congress of Gynecology and Obstetrics, Berlin (West), September 15-20, 1985. \n31-Pregnancy and labor in women over the age of forty years. Accepted and presented at Al-Azhar International Medical Conference, Cairo 28-31 Dec. 1985. \n32-Effect of Copper T intra-uterine device on cervico-vaginal flora. Int. J. Gynaecol. Obstet. 23:2, 153-156, April 1985. \n33-Factors affecting the occurrence of post-Caesarean section febrile morbidity. Population Sciences, 6, 139-149, 1985. \n34-Pre-eclamptic toxaemia and its relation to H.L.A. system. Population Sciences, 6, 131-139, 1985. \n35-The menstrual pattern and occurrence of pregnancy one year after discontinuation of Depo-medroxy progesterone acetate as a postpartum contraceptive. Population Sciences, 6, 105-111, 1985. \n36-The menstrual pattern and side effects of Depo-medroxy progesterone acetate as postpartum contraceptive. Population Sciences, 6, 97-105, 1985. \n37-Actinomyces in the vaginas of women with and without intrauterine contraceptive devices. Population Sciences, 6, 77-85, 1985. \n38-Comparative efficacy of ibuprofen and etamsylate in the treatment of I.U.D. menorrhagia. Population Sciences, 6, 63-77, 1985. \n39-Changes in cervical mucus copper and zinc in women using I.U.D.�s. Population Sciences, 6, 35-41, 1985. \n40-Histochemical study of the endometrium of infertile women. Egypt. J. Histol. 8(1) 63-66, 1985. \n41-Genital flora in pre- and post-menopausal women. Egypt. J. Med. Sci. 4(2), 165-172, 1983. \n42-Evaluation of the vaginal rugae and thickness in 8 different groups. Journal of the Egypt. Soc. of Ob./Gyn. 9:2, 101-114, May 1983. \n43-The effect of menopausal status and conjugated oestrogen therapy on serum cholesterol, triglycerides and electrophoretic lipoprotein patterns. Al-Azhar Medical Journal, 12:2, 113-119, April 1983. \n44-Laparoscopic ventrosuspension: A New Technique. Int. J. Gynaecol. Obstet., 20, 129-31, 1982. \n45-The laparoscope: A useful diagnostic tool in general surgery. Al-Azhar Medical Journal, 11:4, 397-401, Oct. 1982. \n46-The value of the laparoscope in the diagnosis of polycystic ovary. Al-Azhar Medical Journal, 11:2, 153-159, April 1982. \n47-An anaesthetic approach to the management of eclampsia. Ain Shams Medical Journal, accepted for publication 1981. \n48-Laparoscopy on patients with previous lower abdominal surgery. Fertility management edited by E. Osman and M. Wahba 1981. \n49-Heart diseases with pregnancy. Population Sciences, 11, 121-130, 1981. \n50-A study of the biosocial factors affecting perinatal mortality in an Egyptian maternity hospital. Population Sciences, 6, 71-90, 1981. \n51-Pregnancy Wastage. Journal of the Egypt. Soc. of Ob./Gyn. 11:3, 57-67, Sept. 1980. \n52-Analysis of maternal deaths in Egyptian maternity hospitals. Population Sciences, 1, 59-65, 1979. \nArticles published on OBGYN.net: \n1- Abdel Karim M. El Hemaly*, Ibrahim M. Kandil, Laila A. S. Mousa and Mohamad A.K.M.El Hemaly.\nUrethro-vaginoplasty, an innovated operation for the treatment of: Stress Urinary Incontinence (SUI), Detursor Overactivity (DO), Mixed Urinary Incontinence and Anterior Vaginal Wall Descent. \nhttp://www.obgyn.net/urogyn/urogyn.asp?page=/urogyn/articles/ urethro-vaginoplasty_01\n\n2- Abdel Karim M. El Hemaly, Ibrahim M Kandil, Mohamed M. Radwan.\n Urethro-raphy a new technique for surgical management of Stress Urinary Incontinence.\nhttp://www.obgyn.net/urogyn/urogyn.asp?page=/urogyn/articles/\nnew-tech-urethro\n\n3- Abdel Karim M. El Hemaly, Ibrahim M Kandil, Mohamad A. Rizk, Nabil Abdel Maksoud H., Mohamad M. Radwan, Khalid Z. El Shieka, Mohamad A. K. M. El Hemaly, and Ahmad T. El Saban.\nUrethro-raphy The New Operation for the treatment of stress urinary incontinence, SUI, detrusor instability, DI, and mixed-type of urinary incontinence; short and long term results. \nhttp://www.obgyn.net/urogyn/urogyn.asp?page=urogyn/articles/\nurethroraphy-09280\n\n4-Abdel Karim M. El Hemaly, Ibrahim M Kandil, and Bahaa E. El Mohamady. Menopause, and Voiding troubles. \nhttp://www.obgyn.net/displayppt.asp?page=/English/pubs/features/presentations/El-Hemaly03/el-hemaly03-ss\n\n5-El Hemaly AKMA, Mousa L.A. Micturition and Urinary\tContinence. Int J Gynecol Obstet 1996; 42: 291-2. \n\n6-Abdel Karim M. El Hemaly.\n Urinary incontinence in gynecology, a review article.\nhttp://www.obgyn.net/urogyn/urogyn.asp?page=/urogyn/articles/abs-urinary_incotinence_gyn_ehemaly \n\n7-El Hemaly AKMA. Nocturnal Enuresis: Pathogenesis and Treatment. \nInt Urogynecol J Pelvic Floor Dysfunct 1998;9: 129-31.\n \n8-El Hemaly AKMA, Mousa L.A.E. Stress Urinary Incontinence, a New Concept. Eur J Obstet Gynecol Reprod Biol 1996; 68: 129-35. \n\n9- El Hemaly AKMA, Kandil I. M. Stress Urinary Incontinence SUI facts and fiction. Is SUI a puzzle?! http://www.obgyn.net/displayppt.asp?page=/English/pubs/features/presentations/El-Hemaly/el-hemaly-ss\n\n10-Abdel Karim El Hemaly, Nabil Abdel Maksoud, Laila A. Mousa, Ibrahim M. Kandil, Asem Anwar, M.A.K El Hemaly and Bahaa E. El Mohamady. \nEvidence based Facts on the Pathogenesis and Management of SUI. http://www.obgyn.net/displayppt.asp?page=/English/pubs/features/presentations/El-Hemaly02/el-hemaly02-ss\n\n11- Abdel Karim M. El Hemaly*, Ibrahim M. 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