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Dr. Biswas received his Ph.D. from POSTECH, South Korea.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"194151",title:"Dr.",name:"Abhijit",middleName:null,surname:"Biswas",slug:"abhijit-biswas",fullName:"Abhijit Biswas",profilePictureURL:"https://mts.intechopen.com/storage/users/194151/images/system/194151.png",biography:"Dr. Abhijit Biswas is a research associate at the Indian Institute of Science Education and Research (IISER) Pune, in India. His research goal is to design and synthesize highest quality epitaxial heterostructures and superlattices, to play with their internal degrees of freedom to exploit the structure–property relationships, in order to find the next-generation multi-functional materials, in view of applications and of fundamental interest. 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Currently, he is also serving as a reviewer of several reputed peer-review journals.\nDr. Biswas received his B.Sc. in Physics from Kalyani University, followed by M.Sc in Physics (specialization in experimental condensed matter physics) from Indian Institute of Technology (IIT), Bombay. His Ph.D., also in experimental condensed matter physics, was awarded by POSTECH, South Korea for his work on the transport phenomena in perovskite oxide thin films. 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1. Introduction
High spatial resolution remotely sensed imagery helps to obtain quality and detailed information about the earth’s surface features in conjunction with their geographical associations. The internal changeability within the identical land-use land-cover units augments with the rise of resolution. The augmented changeability diminishes the statistical distinguishability of land-use/land-cover classes in the spectral data space. This reduced distinguishability tends to decrease the accuracies of pixel-based clustering algorithms such as Fuzzy C Means [1], minimum distance classifiers [2] and K-Means [3]. These pixel-based clustering techniques assign a pixel to a region according to the similarities of spectral signature. It considers only one pixel at a time [4]. Spectral signatures are the specific combination of emitted, reflected or absorbed electromagnetic (EM) radiation at varying wavelengths which can uniquely identify an object [4].
Compared to IRS-1A/1B sensors, the spectral resolution of high spatial resolution images is normally relatively poor. Spectral resolution describes a sensor’s ability to identify fine intervals of wavelength. The better the spectral resolution, the finer the channel or band width. Therefore, between spatial and spectral resolution, there is a trade-off. It is mainly true for panchromatic (PAN) images of high spatial resolution, namely CARTOSAT-II 1m and IKONOS 1m. There is a need to consider the spatial relationships between pixel values, also known as the ‘texture’ of the scene objects to classify high-resolution (HR) images owing to the wide difference in the spatial structure in these images. Consequently, multiple texture-based clustering technique namely GLCM [5, 6, 7, 8], Markov random field (MRF) model [5], Gray scale rotation invariant [9] were evolved for clustering remote sensing images having high spatial resolution. Nevertheless, above mentioned methods are appropriate in textured area of HR images. A region is called textured; where the intensity dissimilarity within adjacent pixels is substantial. A region is said to be non-textured, where the intensity dissimilarity among adjacent pixels is insignificant [10, 11]. But texture-based classification techniques failed in non-textured region of high spatial resolution image as much variation is not found in the spatial pattern of those regions of the image [12]. Thus, we can infer from earlier studies that classification of high spatial resolution imageries either by pixel or texture-based algorithm may not yield desired results.
Some more techniques namely watershed approach [13, 14], region-growing approach [4, 15], mean shift approach [16, 17], region merging approach [18] etc. are in use for clustering high spatial resolution remote sensing images. Application of these approaches for clustering of images either leads to under-segmentation or over-segmentation [19, 20]. Structural image indexing approach [21], semi-supervised feature learning approach [22] and multi-scale manner using SVM approach [23] are also found fairly suitable in clustering high resolution images. The imagery of higher resolution includes textured and non-textured areas. Hence, pixel or texture-based algorithm for clustering of high-resolution imagery does not produce expected results. This type of high-resolution imagery clustering research is in the trend. Multi-circular local binary pattern and variance-based method [10] were used separately to cluster high resolution image having textured and non-textured regions. The Multi circular local binary pattern operator has been used here for measuring the spatial structure of the image. But, disadvantage in this strategy is that multi-circular local binary pattern operator is susceptible to noise as it exactly sees the value of the moving window’s central pixel as a limit for computing the spatial structure around the central pixel.
In last one decade the Hölder exponent (HE) has been used for calculating spatial structure of the images [24, 25, 26]. It is also being used for clustering high-resolution images [12]. HE gives an evidence of the spatial structure of the image and is not much influenced by the noise. In addition, spatial structure, contrast of the local image holds considerable property for calculating the texture around the pixel. In this research, high-resolution picture textured and non-textured region is originally segmented using HE and VAR-based method and subsequently separately clustered and non-textured areas. VAR is used to calculate the contrast around the pixel. The suggested method is applied with a 1 m spatial resolution on high resolution IKONOS PAN images.
2. Methods
The suggested high-resolution image ‘P’ clustering technique has three main steps: (i) image transformation, (ii) segmentation and extraction, and (iii) clustering. Initially, every pixel of the image is converted into a degree of texture or non-texture around the pixel. In the second step, using segmented image mask, the transformed image is segmented and non-textured and textured regions are extracted from the initial image. Finally, the two areas obtained are separately clustered.
2.1 Transformation of image
The Hölder Exponent (HE) and VAR are jointly used to convert the image for computing the texture. The HE calculates each pixel of P’s spatial structure. Besides spatial structure, local image contrast also grasps important property for computing the texture around the pixel. In this research, therefore, VAR is used to calculate the contrast around the pixel.
2.1.1 Hölder exponent
Hölder exponent has been used for investigating the texture in high-resolution images [12]. It measures the irregularity in the vicinity. Supremacy of applying Hölder Exponent in HR images are that (i) it can be used as an instrument to calculate each pixel of the image’s spatial structure, (ii) no previous data on the pixel intensity is required and (iii) is not very sensitive to noise [12].
Definition of HE [27]: Let μ be a measure on a set Ω as well as for all x Є Ω, э α(x), such that μ (Br(x)) ∼ rα, for small r. Here Br(x) is circle (2D) of radius i centered on x. Then α (x) is called the HE on x.
A sequence of 15 values of radius r (i.e. 1, √2, √5, 3, √13, 3√2, 5, √29, 2√10, 3√5, 7, √61, 6√2, √85, 7√2) centered on x are used as a scale parameter for calculating HE value around each pixel x in the image [12] and the total number (N) of intersected pixels by the perimeter of series of circles of radius r is considered as a scale parameter for computing VAR value around x [12]. N is computed using Eq. (1).
N=∑r=1tmrE1
where t is the total number of identified circles, mr is the number of intersected pixels on the perimeter of the radius r circle.
2.1.2 VAR (σ2) for contrast measurement around each pixel of the image
To get the contrast value of (x, y), the neighbor’s σ2 of each pixel (x, y) is calculated over the entire image. Using Eq. (2), the σ2 (x, y) is realized
σ2xy=∑r=1t∑j=1mrarj−μ2NE2
where arj is the intensity value of pixel (r,j),
μ=∑r=1t∑j=1mrarjN
Thus obtained α(x,y) and σ2 (x,y) for each P(x,y). Afterward, these values are used in Eq. (3) to obtain the corresponding pixel value (x,y) in the transformed image T. Each pixel (x,y) of T signifies the degree of texture around that pixel.
Txy=αxy+σ2xy2E3
2.2 Image segmentation and extraction
The image ‘T’ is segmented into textured and non-textured regions based on a threshold value ‘δ’. The pixel value in T below the ‘δ’ is considered to be a non-textured region, whereas greater than or equal to ‘δ’ is considered to be the textured region in the segmented image. Pixels are labeled as zero in non-textured areas, whereas pixels are marked as one in textured areas in the segmented image mask and depicted as follows:
Γxy=1,Txy≥δ0,Txy<δE4
where T(x,y) and Γ(x,y) represents the pixel value in (x,y) position of the two dimensional transformed image and segmented image respectively and δ represents the threshold value. The δ is calculated by using Eq. (5).
δ=Tmin+Tmax−TminKE5
where Tmin and Tmax represents minimum and maximum pixel gray value in T respectively and K is user defined value.
IKONOS PAN sensor image of size 256 × 256 pixels (shown in Figure 1a) is used to achieve the optimum K. The suggested clustering method is also implemented for distinct K values on this image.
Figure 1.
(a) IKONOS image showing vegetation, built-up area, fallow and water body categories, (b) classified image obtained by applying “HE-VAR and PAN” based method on Figure 1a, (c) classified image obtained by applying “MCLBP and VAR” based method on Figure 1a, (d) classified image obtained by applying “proposed classification method” on Figure 1a, (e) IKONOS image showing fallow, water bodies, vegetation and built-up area categories, (f) classified image obtained by applying “HE-VAR and PAN” based method on Figure 1e, (g) classified images obtained by applying “MCLBP and VAR” based method on Figure 1e, (h) Classified images obtained by applying “proposed classification method” on Figure 1e.
The segmented image is subsequently used to obtain the textured and non-textured region from the initial image P. This process’s mathematical representation is shown as follows:
R1xy=Pxy,Γxy=00,Γxy≠0E6
R2xy=Pxy,Γxy=10,Γxy≠1E7
where P, Γ, R1 and R2 indicates original image, segmented image, extracted non-textured region from original image P and extracted textured region from original image P respectively.
2.3 Clustering
Initially, a threshold is used to segment the transformed image into textured and non-textured region. Afterward, the original image is extracted into textured and non-textured regions using the segmented image mask and clustered independently. The extracted textured region (R2) is clustered by means of ISODATA clustering algorithm [28] considering HE, VAR and intensity values of individual pixel of textured area. The clustering algorithm of ISODATA is less computational, easy and non-supervisory. Whereas the non-textured area (R1) of the image is categorized using the clustering algorithm of ISODATA. In the event of non-textured region, the individual pixel HE and VAR value is not regarded for classification as there is no important variation in texture between classes. The classified outputs of the non-textured and textured region are subsequently produced separately and mixed together to obtain the final classified image.
This research uses “HE-VAR and PAN” and “MCLBP and VAR” based clustering technique to show the power of the suggested clustering technique. The technique based on “HE-VAR and PAN” clusters the entire image using the HE, VAR and intensity of each pixel of the IKONOS PAN image. The suggested technique of clustering is then contrasted with the outcomes of the clustering method based on “HE-VAR and PAN” and “MCLBP and VAR” to demonstrate the strength of the suggested technique of clustering.
3. Results and discussion
The projected clustering method imagines threshold δ to get the segmented image mask from the transformed image. The threshold is computed using a constant ‘K’. In this study, proposed clustering procedure is implemented on IKONOS PAN image with spatial resolution 1 m for ‘K’ values between 3 and 7 and subsequently, classification rate is measured for these ‘K’ values using the ground truth data. The classification accuracy with different ‘K’ is shown in Figure 2. The ‘K’ affects the accuracy in classifying High spatial resolution images considerably as shown in Figure 2. For computing texture, a suitable choice of ‘K’ is important. In this study, superlative performance in high-resolution image classification was accomplished with K = 5. The optimum K is discovered based on Figure 1a and is also implemented in the classification of Figure 1e in addition to other images and found classification accuracy is more than 88%. Thus, from the present study, we can infer that the same K value is suitable for most images.
Figure 2.
Classification accuracy as a function of K.
The Proposed clustering method, “MCLBP and VAR” based method and “HE-VAR and PAN” based method were applied on two different 1 m PAN (IKONOS) images (size 256 × 256 pixels) covering (i) vegetation, (ii) built-up area, (iii) water bodies, and (iv) fallow (shown in Figure 1a,e). Texture is observable in in Figure 1a,e. The results of proposed method are then compared with the results obtained from the analysis based on “HE-VAR and PAN” and “MCLBP and VAR” respectively.
Figure 1f–h shows the classification outcomes of the methods “HE-VAR and PAN,” “MCLBP and VAR” and “Proposed classification” after proceeding to the second IKONOS image respectively. Figure 1b–d shows the classification outcomes of the methods “HE-VAR and PAN,” “MCLBP and VAR” and “Proposed classification” after proceeding to the first IKONOS image respectively. Classified images recognize varied features in Figure 1b–d,f–h. From the results, it is evident that the method based on “MCLBP and VAR” gives less heterogeneous segments than the method based on “HE-VAR and PAN,” while the method based on “Proposed classification method” provides more homogeneous segments with distinct classes than the method based on “MCLBP and VAR.”
The ground truth data is collected using GPS equipment for the class vegetation, built-up area, fallow and water body of sample size of 656, 519, 577 and 462 square meters respectively. Afterward, ArcGIS software is used to transfer the ground truth data into vector data. Subsequently, by overlaying the ground truth information distinctly on the results acquired from both IKONOS images (Figure 1a,e) adopting methods such as “HE-VAR and PAN,” “MCLBP and VAR” and “Proposed clustering,” the classification accuracies for each strategy are shown by confusion matrix. The confusion matrices (Table 1) calculated for Figure 1b–d showed that the precision of classification of vegetation, built-up area, fallow and water bodies is (73, 69, 59 and 87% respectively) based on the ‘HE-VAR and PAN’ technique and (79, 71, 68 and 89% respectively) based on the ‘MCLBP and VAR ‘technique, whereas (91, 86, 85 and 94% respectively) by the “Proposed clustering” method. Table 2 demonstrates the confusion matrices calculated for Figure 1f–h showed that the precision of classification of vegetation, built-up area, fallow and water bodies is (73, 74, 66 and 88% respectively) based on the ‘HE-VAR and PAN ‘technique and (78, 76, 68 and 89% respectively) based on the ‘MCLBP and VAR ‘technique whereas (90, 87, 86 and 93% respectively) by the “Proposed clustering” method.
Classes derived from satellites
Classification method
Grand observed class
Vegetation
Built-up area
Fallow
Water body
Row total
Classification accuracy (%)
C (%)
O (%)
HE-VAR and PAN
Vegetation
251
35
12
7
305
72.75
27.24
15.65
Built-up area
18
163
14
5
200
69.07
30.93
15.68
Fallow
53
23
144
14
234
59.26
40.74
37.04
Water body
23
15
73
177
288
87.19
12.81
54.67
Column total
345
236
243
203
1027
MCLBP and VAR
Vegetation
272
31
11
4
332
78.84
18.26
13.33
Built-up area
17
168
7
3
205
71.25
28.81
11.41
Fallow
43
21
166
15
223
68.33
31.69
32.51
Water body
13
16
59
181
258
89.28
10.83
43.35
Column total
345
236
243
203
1027
Proposed method
Vegetation
313
17
7
3
340
90.85
9.27
7.82
Built-up area
7
204
8
2
221
86.28
13.56
7.20
Fallow
19
7
206
7
239
84.77
15.23
13.58
Water body
6
8
22
191
227
94.11
5.91
17.73
Column total
345
236
243
203
1027
Table 1.
The confusion matrices showing the classification accuracy obtained by applying “HE-VAR and PAN”, “MCLBP and VAR” and “Proposed” methods separately on IKONOS image shown in Figure 1a.
C: Commission error, O: Omission error.
Classes derived from satellites
Classification method
Grand observed class
Vegetation
Built-up area
Fallow
Water body
Row total
Classification accuracy (%)
C (%)
O (%)
HE-VAR and PAN
Vegetation
228
15
43
16
302
73.3
26.68
23.79
Built-up area
32
209
14
5
260
73.7
26.14
18.02
Fallow
27
27
221
11
286
66.3
33.83
19.46
Water body
24
32
56
227
339
87.6
12.35
43.24
Column total
311
283
334
259
1187
MCLBP and VAR
Vegetation
241
13
38
9
301
77.6
22.51
20.58
Built-up area
29
215
13
7
264
75.83
24.02
17.31
Fallow
26
26
228
11
291
68.36
31.74
18.86
Water body
15
29
55
232
331
89.4
10.42
38.22
Column total
311
283
334
259
1187
Proposed method
Vegetation
281
7
14
6
308
90.4
9.64
8.68
Built-up area
3
244
9
5
261
86.7
13.78
6.0
Fallow
15
15
287
7
324
85.8
14.07
11.07
Water body
12
17
24
241
294
93.2
6.94
20.46
Column total
311
283
334
259
1187
Table 2.
The confusion matrices showing the classification accuracy obtained by applying “HE-VAR and PAN”, “MCLBP and VAR” and “Proposed” methods separately on IKONOS image shown in Figure 1e.
C: Commission error, O: Omission error.
The categorized result for Figure 1a,e shows that the “HE-VAR and PAN” method under segment as a result (i) fallow assorted with water bodies shown in Figure 1b,f, (ii) built-up region assorted with fallow and vegetation shown in Figure 1f, (iii) vegetation assorted with water bodies shown in Figure 1b,f, (iv) fallow assorted with built-up region shown in Figure 1b. This incoherence decreases vegetation, fallow, water bodies and built-up area classification precision as shown in Tables 1 and 2. The technique based on “MCLBP and VAR” somehow overcomes these inconsistencies. It is discovered that, as shown in Figure 1c,g, the superposition of fallow, water body, vegetation region becomes less. In addition, decreased inconsistencies improve the accuracy of the classification of fallow, water body and vegetation regions (see Tables 1 and 2).
“HE-VAR and PAN” based method classifies water bodies and fallow areas as a single class (Figure 1b,f) since the texture patterns of these two areas does not show much difference in high resolution imageries as shown in Figure 1a,e. “MCLBP and VAR” based technique demonstrates improvement in classifying the fallow areas and water bodies which is observable in Figure 1g. But this method could not extract non-textured region appropriately form Figure 1a since MCLBP is sensitive to noise. Therefore “MCLBP and VAR” based method could not discriminate appropriately fallow areas and water bodies in Figure 1a as visible in Figure 1c. HE is not as much of sensitive to noise therefore the proposed technique partitions the image into textured and non-textured regions noticeably which in turn helps in classifying the fallow and water bodies as shown in Figure 1d.
The proposed clustering method is applied further on a 1 m PAN (IKONOS) image (Figure 3a) of (i) urban woodland, (ii) building, (iii) water bodies, and (iv) fallow to show the robustness and validity of the method in classifying land use area. The method satisfactorily discriminate urban woodland, building, fallow and water bodies as shown in Figure 3b. The algorithm also implemented on two extra 1 m PAN (IKONOS) images: (i) Figure 4a of fallow, vegetation, built-up area and bare land and (ii) Figure 4c of water, vegetation, fallow and built-up area. The findings (Figure 4b,d) show that vegetation, fallow, built-up region, bare soil and water bodies are satisfactorily discriminated against by the algorithm.
Figure 3.
(a) IKONOS image showing urban woodland, building, water body and fallow categories, (b) classified image obtained by applying “proposed classification method” on Figure 3a.
Figure 4.
(a) IKONOS image showing fallow, built-up area, vegetation and bare soil categories, (b) classified image obtained by applying “proposed classification method” on Figure 4a, (c) IKONOS image showing vegetation, fallow, built-up area and water bodies categories, (d) classified image obtained by applying “proposed classification method” on Figure 4c.
4. Conclusion
In the present study, the spatial structure of local image texture is computed using HE. The contrast around the pixel is measured using VAR. Afterward, the image is transformed using HE and VAR together for measuring the texture. A threshold δ is used to extract textured and non-textured region from the image. The classification algorithm ISODATA is used to classify the textured region taking into account HE, VAR and intensity values of the textured area’s individual pixels. Whereas ISODATA clustering algorithm classifies the extracted non-textured region of the image. The HE and VAR value of individual pixels is not regarded for classification in the event of non-textured region. From the research outcomes, it is discovered that the suggested technique is helpful to extract earth surface characteristics from complicated remote sensing images that contain both textured and non-textured areas. Moreover, it can be considered as an intuitively appealing and unsupervised clustering algorithm for extracting features from remotely sensed images. As a result, the method is potentially useful to extract earth surface features by clustering high spatial resolution panchromatic images more efficiently.
Acknowledgments
The author sincerely thanks the Director, NRSC, Hyderabad, India and CGM, RCs, NRSC, Hyderabad, India for their support. The author is also grateful to the former GM, RRSC-East for support.
\n',keywords:"remote sensing image, clustering, classification, land use, land cover, features, extraction",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/69795.pdf",chapterXML:"https://mts.intechopen.com/source/xml/69795.xml",downloadPdfUrl:"/chapter/pdf-download/69795",previewPdfUrl:"/chapter/pdf-preview/69795",totalDownloads:243,totalViews:0,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,dateSubmitted:"June 3rd 2019",dateReviewed:"August 13th 2019",datePrePublished:"October 29th 2019",datePublished:"November 11th 2020",dateFinished:null,readingETA:"0",abstract:"Image processing is growing fast and persistently. The idea of remotely sensed image clustering is to categorize the image into meaningful land use land cover classes with respect to a particular application. Image clustering is a technique to group an image into units or categories that are homogeneous with respect to one or more characteristics. There are many algorithms and techniques that have been developed to solve image clustering problems, though, none of the method is a general solution. This chapter will highlight the various clustering techniques that bring together the current development on clustering and explores the potentiality of those techniques in extracting earth surface features information from high spatial resolution remotely sensed imageries. It also will provide an insight about the existing mathematical methods and its application to image clustering. Special emphasis will be given on Hölder exponent (HE) and Variance (VAR). HE and VAR are well-established techniques for texture analysis. This chapter will highlight about the Hölder exponent and variance-based clustering method for classifying land use/land cover in high spatial resolution remotely sensed images.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/69795",risUrl:"/chapter/ris/69795",book:{slug:"geographic-information-systems-in-geospatial-intelligence"},signatures:"Debasish Chakraborty",authors:[{id:"307075",title:"Dr.",name:"Debasish",middleName:null,surname:"Chakraborty",fullName:"Debasish Chakraborty",slug:"debasish-chakraborty",email:"deba.isro@gmail.com",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Methods",level:"1"},{id:"sec_2_2",title:"2.1 Transformation of image",level:"2"},{id:"sec_2_3",title:"2.1.1 Hölder exponent",level:"3"},{id:"sec_3_3",title:"2.1.2 VAR (σ2) for contrast measurement around each pixel of the image",level:"3"},{id:"sec_5_2",title:"2.2 Image segmentation and extraction",level:"2"},{id:"sec_6_2",title:"2.3 Clustering",level:"2"},{id:"sec_8",title:"3. Results and discussion",level:"1"},{id:"sec_9",title:"4. Conclusion",level:"1"},{id:"sec_10",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'Bezdek JC, Ehrlich R, Full W. FCM: The Fuzzy C-Means clustering algorithm. Computers and Geosciences. 1984;10:191-203'},{id:"B2",body:'Richards JA. Remote Sensing Digital Image Analysis: An Introduction. Berlin, Heidelberg: Springer-Verlag; 1995. pp. 265-290'},{id:"B3",body:'Hartigan JA, Wong MA. A K-means clustering algorithm. Applied Statistics. 1979;28(1):100-108'},{id:"B4",body:'Chakraborty D, Sen GK, Hazra S. Image Segmentation Techniques. 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Image segmentation based on constrained spectral variance difference and edge penalty. Remote Sensing. 2015;7:5980-6004'},{id:"B20",body:'Wang C, Shi AY, Wang X, Wu FM, Huang FC, Xu LZ. A novel multi-scale segmentation algorithm for high resolution remote sensing images based on wavelet transform and improved JSEG algorithm. Optik - International Journal for Light and Electron Optics. 2014;125(19):5588-5595'},{id:"B21",body:'Xia GS, Yang W, Delon J, Gousseau Y, Sun H, Maître H. Structural high-resolution satellite image indexing. In: ISPRS TC VII Symposium-100 Years ISPRS, Vol. 38; 2010. pp. 298-303'},{id:"B22",body:'Yang W, Yin X, Xia GS. Learning high-level features for satellite image classification with limited labeled samples. IEEE Transactions on Geoscience and Remote Sensing. 2015;53(8):4472-4482'},{id:"B23",body:'Huang X, Zhang L. An SVM ensemble approach combining spectral, structural, and semantic features for the classification of high-resolution remotely sensed imagery. IEEE Transactions on Geoscience and Remote Sensing. 2013;51(1):257-272'},{id:"B24",body:'Lucieer A, Stein A, Fisher P. Multivariate texture-based segmentation of remotely sensed imagery for extraction of objects and their uncertainty. International Journal of Remote Sensing. 2005;26(14):2917-2936'},{id:"B25",body:'Malladi RK, Kasilingam D, Costa AH. Speckle filtering of SAR images using Hölder regularity analysis of the sparse code. In: IEEE International Geoscience and Remote Sensing Symposium; 2003. pp. 63998-64000'},{id:"B26",body:'Tahiri AM, Farssi SM, Touzani A. Textures in Images Classification Using a Multifractal Approach. IEEE SITIS. 2005. Available from: http://www.u-bourgogne.fr/SITIS/05/download/Proceedings/Files/f138.pdf'},{id:"B27",body:'Bourissou A, Pham K, Levy-Vehel J. A multifractal approach for terrain characterization and classification on SAR images. IGARSS. 1994;3:1609-1611'},{id:"B28",body:'Jain AK, Murty MN, Flynn PJ. Data clustering: A review. ACM Computing Surveys. 1999;31(3):264-323'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Debasish Chakraborty",address:"deba.isro@gmail.com",affiliation:'
Regional Remote Sensing Centre-East, National Remote Sensing Centre, ISRO, New Town, Kolkata, West Bengal, India
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1. Introduction
“Leadership is not only about individuals, but also about teams” [1].
Nowadays, leadership in health services is an important issue that aims to protect and improve human health. Rapid changes and developments in the health sector increase the importance of developing managership and leadership skills for health managers [2]. Regional and national health systems tend to redesign their functions and priorities by making structural changes in social and economic terms to cope with the increasing health problems [3]. The inclusion of complex technology and intense human relations in hospital services, which constitute a significant part of health care services, leads to the emergence of important managerial problems [4]. The existence of effective, creative, visionary, motivated, knowledgeable, principled leaders for the development of the institution is important to eliminate various problems in health services. To be able to do this, it is necessary to determine the qualifications that can contribute to the effective leadership of corporate managers [2, 5].
Leadership in health services is of great importance in terms of following innovations and adapting to existing situations [6]. Leadership can be defined as a multidimensional process, which means that a person motivates others to direct their activities and develop their skills under certain circumstances [3, 7]. The leader is the person who sets the goals of his group and who influences and directs the members of the group in line with these goals [8]. In addition, a good leader must be dynamic, passionate, have a motivational effect on other people, be solution oriented, and try to inspire others. Nurses, who work together with other health personnel in hospitals, constitute an important group in leadership. Nursing, which is a key force for patient safety and safe care, is a human-centered profession, and therefore leadership is a key skill for nurses at all levels. The leadership styles of nurse managers are believed to be an important determinant of job satisfaction and job commitment of nurses. Nurses who are mobilized and empowered to perform specific personal or group goals by a good leader nurse are willing to implement evidence-based practices and are highly motivated, well informed, and committed to organizational goals. Therefore, they perform patient care in a more effective and planned process. It has become imperative to examine the role of leadership styles of nurse managers on staff outcomes after miscarriage of health workforce, which is a global nursing problem, increasing health care costs and workload [9, 10].
There is a limited number of articles in the literature about the leadership styles of nurses. In these studies, the importance of leadership styles and practices on patient outcomes and patient safety, health service power and corporate culture were determined [3]. However, Cummings [10] stated that most styles can be grouped under relational leadership or task-focused leadership. Relational leadership styles focus on people and relationships. It includes transformational, emotional intelligence, resonance, and participatory leadership. These styles are positively associated with staff satisfaction, organizational commitment, improved staff health welfare, stress reduction, job satisfaction, productivity increase, effective study, and positive patient outcomes. However, task-focused leadership is focused on completion of works, deadlines, and directives. Task-focused leadership styles include operational, autocratic, and laissez-faire leadership [10].
2. Leadership styles in nursing
2.1 Relational leadership
Relational leadership styles focus on people and relationships and include transformational, emotional intelligence, resonance, and participatory leadership [11]. These leadership styles are associated with increased employee satisfaction, organizational commitment, improved staff health and well-being, stress reduction, job satisfaction, increased productivity, effective work, and positive patient outcomes [10].
2.1.1 Transformational leadership
Transformational leadership is considered the gold standard of leadership [11]. Transformational leadership is at the center of nursing because it has an impact on patient outcomes, employee satisfaction, and safety culture. Transformational nurse leaders first perform nursing, communicate effectively with their audiences, and become effective role models [12]. Such leaders are motivated and empowering, encouraging and following their audience for organizational goals and individual goals [13, 20]. In addition, it is explained how the transformational leaders have four characteristics that affect their audience. These characteristics are charisma, inspirational, intellectual thinking, and individual attention [42].
It is thought that the transformational leaders fascinate their audience with the charisma feature. This fascination is sometimes associated with the physical characteristics of the leader as well as communication skills and vision. The inspiring character of transformational leaders supports and motivates their followers with encouraging speeches in case of hard work and crises [14].
Transformational leaders, with their intellectual characteristics, encourage their followers to think innovatively and to think about how we can do it better. At the same time, these leaders do not prefer their followers to accept their thoughts as they are [14]. Finally, the transformational leaders, who are interested in their followers individually, advise them in line with their individual needs. In addition, leaders appreciate their followers within the team.
When considered with a general assessment, transformational leaders think that their followers should be evaluated individually and the needs and characteristics of the followers may change with the influence of the leader. Therefore, with the mentoring of the leader, the development of the followers increases at the same rate.
2.1.2 Resonance leadership
Resonance leadership is based on emotional intelligence and awareness, including being open and sensitive to judgment [15]. Resonance leaders have emotional intelligence features. These are self-awareness, self-management, social awareness, and relationship management [16]. According to these characteristics, resonance leaders are effective in managing and solving conflict, democratic, collaborative, and can find solutions to problems.
2.1.3 Emotional intelligence
Emotional intelligence was first described as a feature of transformational and resonant leadership in the 1980s. Leaders with emotional intelligence have four important structures: self-awareness, self-management, social awareness, and social skills. Emotionally intelligent leaders are sensitive to the well-being, emotions, and emotional health of themselves and their followers, and develop effective personal relationships while directing followers to common business goals. Emotionally intelligent leaders manage and reflect their emotions, making rational decisions to ensure teamwork and collaboration. Emotionally intelligent leaders are also effective in conflict resolution because they have the ability to see the situation from others’ perspective and manage work stress [11].
2.1.4 Participatory leadership
In participatory leadership, the views of individuals and groups are taken into consideration. Knowledge, experience, skills, and innovation are of great importance in the decision-making process, with a wide range of expertise and participation in engagement. In 2016, WHO called for participatory leadership to replace hierarchical leadership models of health leadership, suggesting that inclusiveness and the involvement of various stakeholders would strengthen health services [17].
2.2 Task-focused leadership
The task-focused leadership style involves planning business activities, clarifying roles within a team or a group of people, as well as a set of objectives, and continuous monitoring of processes and performance. Task-focused leaders focus on completion of jobs, deadlines, and directives [10]. Task-focused leadership is significantly associated with high-level patient satisfaction [18].
2.2.1 Transactional and autocratic leadership
“Do it now!”
This concept, which is referred to as “transactional leadership” in English literature, is used as “interactionist,” “operational,” or “transactional” leadership in different sources. Transactional leadership is a leadership style that provides short-term goals and motivates viewers through the fulfillment of individual needs in exchange for high performance toward organizational goals [19]. Leaders in transactional leadership act as exchanges managers by exchanging followers who lead to improvement in production, and are interested in processes rather than shared values with forward-thinking ideas [18, 20].
Transactional leadership style emerges in two basic forms as “management with exceptions” and “conditional rewarding” [21, 22]. The form management with exceptions is divided into two as active and passive. The active leader monitors the performance of the team followers and intervenes to correct these errors when he/she detects errors. The passive leader expects the followers’ mistakes to draw their attention before giving negative feedback or any warning [23]. In conditional rewarding, transactional leaders clearly explain to their followers what their duties are, how they will be made, and how they will be rewarded if the desired tasks are fulfilled satisfactorily [21, 24].
Transactional leaders are cultural carriers who maintain the existing order and act in line with traditions and past [25]. In crises where an explicit orientation is required, the transactional leadership approach is an effective style. Transactional leadership can be the best leadership style for the direction of critical events [18, 26]. This leadership style can be effective in emergency situations such as cardiac arrest, by enabling nurses to focus on the task as a whole on the patient [27].
In the literature, transactional leadership and transformational leadership are explained together and comparisons are made. Besides, unlike the transformational leadership, leaders who adopt an interactive approach want to maintain the same things instead of changing the future, and they are less concerned with the creative and innovative aspects and focus on concepts such as efficiency and quality [28]. Bass emphasizes the use of interactive leadership as a conditionally rewarding performance, especially among followers and leaders [29]. While transformational leadership results in a performance beyond expected, interactive leadership focuses on the expected results [30]. According to the transactional leadership, leadership is seen as a simple mutual exchange between leaders and followers based on economic or political reasons, while transformational leadership states that leaders and followers influence each other in order to achieve higher levels of motivation and morale [31].
Another type of transactional leadership is autocratic leadership. Autocratic leaders are defined as directives, controlling, power-oriented, and closed-minded. The leader describes the “what, when, why, and how” of the task. He/she emphasizes obedience, loyalty, and strict adherence to the rules. Followers do what the autocratic leader says [32]. The autocratic leadership style can be considered ideal in emergencies because he or she takes all decisions himself/herself, regardless of the views of the leading staff [3]. Because information is seen as power, critical information can be hidden from the team. Mistakes are not tolerated and individuals are accused rather than erroneous operations. Rewards are given for compliance, but disobedience is punished [18, 32]. In addition, autocratic leaders can create fear among staff and often make decisions without consulting the team [32]. These leaders motivate their subordinates by using their “legal powers,” “rewarding powers,” and “coercive forces.” Autocratic leaders may not be welcome by their team, but this can be transformed into appreciation and devotion when the positive results of their leadership emerge. Although staff do not like autocratic leaders, they often work well on their orders [18, 32]. This leadership approach can be useful at the moment when it is necessary to make quick decisions or to mobilize uneducated and less-motivated followers in the short term by pressure and fear [6, 33, 34]. The positive aspect of this style is that it works perfectly in emergencies or chaotic situations with little time for discussion.
Schoel et al. found that very popular leaders were perceived as ineffective, while unpopular leaders could be perceived as effective [35]. According to the results of Uysal et al., the perception of the behavior of hospital managers as autocratic by followers decreases the productivity of the work [6], because autocratic leadership is perceived negatively by the followers; the reason is that the authoritarian attitude does not give the employee the right to speak, and that the awards and punishments are precise and clear.
2.2.2 Laissez-faire leadership
The style of leadership recognizing full freedom is also referred to as “laissez-faire” in the literature and is expressed as “let them do it.” This kind of a leader advises the process by not participating in the process, encourages followers to generate ideas, offers suggestions when asked by followers, and declares opinions. [31]. Leadership that recognizes full freedom is a style in which the leader provides little or no orientation or control, and prefers a practical approach. Fully free leadership style includes a leader who does not decide, and acts without staffing or supervision [3]. The main task of the leader is to provide resources. Such leaders dissipate responsibilities and retreat and refrain from taking decisions [31]. The leader only gives his/her opinion when asked about his/her opinion on any subject, but this view is not binding on his/her followers [36].
Leadership that recognizes full freedom is an authoritative, task-focused leadership style, because it involves the regulation of tasks in times of crisis, so it shows reactive leadership. This style of leadership is often used by inexperienced leaders or those who are about to vacate their leadership positions, who prefer to give up their followers or others to change their positions, such as those who would like to give up their job [18]. The leader leaves the followers on their own. Followers do what they think is the best. Followers are trained to find the best solution to their problems. Whenever he/she sees it necessary, a person can form a group with whom he/she wants to solve problems, try new ideas, and make the decisions that he/she thinks are most appropriate for him/her [37, 38].
There are positive and negative aspects of the leadership style that gives full freedom. The first positive aspect of this leadership style is the determination and implementation of the goals, plans, and policies of employees or members of the organization, and it mobilizes the creativity of each member or employee [39]. The second positive aspect is that employees are motivated to train themselves and find the most appropriate solution to the problems. When the individual deems it necessary, he/she creates a group with the people he/she wants, solves the problems, tries new ideas, and reaches the most appropriate decisions [40]. The negative aspects of leadership, which gives full freedom, are the emergence of turmoil within the organization and the fact that everyone leads to the targets he/she wants and even toward opposing targets. Another disadvantage is the significant decrease in organizational success, independent of personal achievements.
Skogstad et al. state that the type of leadership recognizing full liberty reinforces the role conflict and role ambiguity experienced by the individual, and increases the conflicts with colleagues [40]. Hinkin et al. also state that leadership behaviors that recognize full liberty harm the punitive and rewarding roles of the leader and decrease leaders’ effectiveness [41]. Chaudhry and Javed state that fully free leadership has no effect on the motivation of the followers compared to other types of leadership [42]. Şentürk et al. reveals that fully free leadership does not have a reinforcing effect on innovative behaviors but rather reduces it [31]. According to the results of Uysal et al., the perception of the behavior of hospital managers as autocratic by followers decreases the productivity of the work [6]. Because autocratic leadership is perceived negatively by the followers. The reason is that the authoritarian attitude does not give the employee the right to speak, and that the awards and punishments are precise and clear.
2.2.3 Instrumental leadership
Instrumental leadership focuses on choosing an appropriate strategy along with appropriate resources to achieve business goals, and it is vital for sustainable corporate performance [43, 44]. This leadership style is part of the spectrum of transformational and interactive leadership styles. Instrumental leaders can be effective managers because they ensure efficiency protection. Thus, jobs are completed in line with the resources, strategic vision, and time constraints of the health facility [45]. In current leadership approaches, the strategy and task-focused developmental functions of the leaders are not taken into account; however, strategy and task-focused functions, which are instrumental forms of leadership, are essential for organizations and followers to ensure sustainable performance. Instrumental leadership is based on neither ideals nor swap relationships. Instrumental leadership includes ensuring harmony between the organization and the environment, developing strategies, preparing task and strategy tables, using resources effectively, and providing performance feedback [44]. The most prominent feature of the instrumental leadership type is the determination of the subordinates’ path by the leader [34]. The instrumental leader is mainly concerned with the timely completion of the work related to the desired goal; it focuses on functions such as setting goals, organizing group members, setting up the communication system, and determining work-related times [46]. Akyurt et al. found that instrumental and interactive leadership have a statistically significant and positive effect on job satisfaction and organizational commitment [21]. Tengilimoglu and Yigit, in their study on 355 state hospital workers in order to determine the effect of leadership behavior in hospitals on job satisfaction of the employees, found that the leadership style with the highest job satisfaction were participatory, instrumental, success-oriented, and supporting leadership, respectively [34].
3. Effective leadership
As the health sector is in a process of change, new leadership approaches need to be implemented to effectively manage this new structure [46]. Developments in the field of management-organization and organizational behavior and new concepts have also led to the emergence of new leadership styles in leadership [4]. Leadership is important for every organization as well as for health organizations, because the success of an organization is a good leader [47]. For effective leadership, it is important to focus on the dynamic relationships between guidance, leadership values, culture, talent, and organizational context [48]. Effective leaders in health care services consider safe, qualified, and friendly care as the top priority. Effective leadership is critical to facilitate quality care, patient safety, and positive staff development. Leaders make the voice of patients continuous; they continuously monitor their patient experiences, concerns, needs, and feedback [49]. Nurses, the largest workforce in a health institution and a dynamic profession, play an important role in health leadership and policy-making, while maintaining their traditional care skills [50]. The leadership style of executive nurses plays an important role in the provision of job satisfaction and motivation of nurses, development of institutional commitment, and effective management of conflicts [51, 52, 53]. In addition, effective leadership styles can increase the quality of health care outcomes. In addition, leadership in health facilities is considered as an important factor in ensuring quality health services, patient satisfaction, and financial performance.
4. Conclusion
Nurses are responsible for guiding the community because of their responsibilities in health care. Patient care and education, effective communication, and clinical management are the most important tasks. These tasks are closely related to leadership behavior. Nurses who exhibit leadership behavior will be pioneers in bringing the profession to a professional level. The goal of future health care institutions should be to influence the quality of patient care through a good nursing leadership. Future research should focus on the development, applicability, and implementation of robust leadership style models in different health environments. These studies should include multidisciplinary professional teams; strengthen the role of nurses and other health professionals; and address organizational parameters and individual wishes, preferences, and expectations for quality of life and health care.
Acknowledgments
We thank everyone who provided scientific guidance.
Conflict of interest
The authors declare no conflict of interest.
\n',keywords:"nursing, leadership, leadership styles, patient safety, quality of care",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/69876.pdf",chapterXML:"https://mts.intechopen.com/source/xml/69876.xml",downloadPdfUrl:"/chapter/pdf-download/69876",previewPdfUrl:"/chapter/pdf-preview/69876",totalDownloads:572,totalViews:0,totalCrossrefCites:0,dateSubmitted:"April 11th 2019",dateReviewed:"September 11th 2019",datePrePublished:"November 4th 2019",datePublished:null,dateFinished:"November 4th 2019",readingETA:"0",abstract:"Recent developments in the field of management-organization and organizational behavior and new concepts have also led to the emergence of new leadership styles in leadership. Leadership in health services is important for following innovations and adapting to current situations. Nurses working together with other health personnel in hospitals providing health services constitute an important group in leadership. Nursing, which is a key force for patient safety and safe care, is a human-centered profession, and therefore leadership is a key skill for nurses at all levels. The leadership styles of nurse managers are believed to be an important determinant of job satisfaction and persistence of nurses. The need for nurses with leadership skills and the need for nurses to develop their leadership skills are increasing day by day. There are several leadership styles defined in nursing literature. These leadership styles are examined under the titles of relational leadership style, transformational leadership, resonant leadership, emotional intelligence leadership, and participatory leadership. The task-focused leadership style is explored under the headings of transactional and autocratic leadership, laissez-faire leadership, and instrumental leadership.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/69876",risUrl:"/chapter/ris/69876",signatures:"Serpil Çelik Durmuş and Kamile Kırca",book:{id:"9047",title:"Nursing",subtitle:"New Perspectives",fullTitle:"Nursing - New Perspectives",slug:"nursing-new-perspectives",publishedDate:"December 16th 2020",bookSignature:"Serpil Çelik Durmuş",coverURL:"https://cdn.intechopen.com/books/images_new/9047.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"189558",title:"Ph.D.",name:"Serpil",middleName:null,surname:"Çelik Durmuş",slug:"serpil-celik-durmus",fullName:"Serpil Çelik Durmuş"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Leadership styles in nursing",level:"1"},{id:"sec_2_2",title:"2.1 Relational leadership",level:"2"},{id:"sec_2_3",title:"2.1.1 Transformational leadership",level:"3"},{id:"sec_3_3",title:"2.1.2 Resonance leadership",level:"3"},{id:"sec_4_3",title:"2.1.3 Emotional intelligence",level:"3"},{id:"sec_5_3",title:"2.1.4 Participatory leadership",level:"3"},{id:"sec_7_2",title:"2.2 Task-focused leadership",level:"2"},{id:"sec_7_3",title:"2.2.1 Transactional and autocratic leadership",level:"3"},{id:"sec_8_3",title:"2.2.2 Laissez-faire leadership",level:"3"},{id:"sec_9_3",title:"2.2.3 Instrumental leadership",level:"3"},{id:"sec_12",title:"3. Effective leadership",level:"1"},{id:"sec_13",title:"4. Conclusion",level:"1"},{id:"sec_14",title:"Acknowledgments",level:"1"},{id:"sec_17",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Darzi, A. High Quality Care For All: NHS Next Stage Review Final Report. [Internet]. 2008. 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The Effect of Leadership Behaviors of Clinical Responsible Nurses on Nurses’ Job Satisfaction. (Thesis). Halic University, Institute of Health Sciences. İstanbul. 2009'},{id:"B52",body:'Bucak B. The Perceptions of Leadership Approaches and Conflict Management Strategies of Nurses Who Work in Two Different Hospitals in Ankara. (Thesis). Gazi University Institute of Health Sciences. Ankara. 2010'},{id:"B53",body:'Gülkaya G. Transformative Leadership Behaviors of Nurses in Service and Motivation Status of Nurses Working Together. (Thesis). Hacettepe University, Institute of Health Sciences. Ankara. 2012'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Serpil Çelik Durmuş",address:"serpilcelik2010@gmail.com",affiliation:'
Nursing Management Department, Faculty of Health Sciences, Kırıkkale University, Turkey
Nursing Department, Faculty of Health Sciences, Kırıkkale University, Turkey
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