8-Band Image Data Processing of the Worldview-2 Satellite in a Wide Area of Applications

Recent years have seen advances in remote sensing in many fields with applications at a spatial scale which range from global to local. As a consequence, the need to observe the Earth with more specialized and sophisticated sensors and data analysis techniques to obtain more accurate information has increased. On the 8th October 2009 a new second nextgeneration Worldview-2 satellite was launched by DigitalGlobe: it represents the latest innovation among sensors for the acquisition of remote sensed imagery. It has an advanced agility due to control moment gyros (like Worldview-1) and combines an average revisiting time of 1.1 days around the globe with a large scale collection capacity. Moreover, it is also the first commercial satellite able to provide panchromatic imagery at 46 cm of spatial resolution and 8-band multispectral imagery at 1.84 m spatial resolution. In addition to the standard panchromatic and multispectral BLUE, GREEN, RED and NEAR INFRARED (NIR1) bands the Worldview-2 sensor has:


Introduction
Recent years have seen advances in remote sensing in many fields with applications at a spatial scale which range from global to local. As a consequence, the need to observe the Earth with more specialized and sophisticated sensors and data analysis techniques to obtain more accurate information has increased. On the 8 th October 2009 a new second nextgeneration Worldview-2 satellite was launched by DigitalGlobe: it represents the latest innovation among sensors for the acquisition of remote sensed imagery. It has an advanced agility due to control moment gyros (like Worldview-1) and combines an average revisiting time of 1.1 days around the globe with a large scale collection capacity. Moreover, it is also the first commercial satellite able to provide panchromatic imagery at 46 cm of spatial resolution and 8-band multispectral imagery at 1.84 m spatial resolution. In addition to the standard panchromatic and multispectral BLUE, GREEN, RED and NEAR INFRARED (NIR1) bands the Worldview-2 sensor has: 1. a shorter wavelength blue band, COASTAL, ranging from 400 to 450 nm, planned for bathymetric studies, for water color analyses and substantially influenced by atmospheric scattering; 2. a YELLOW band, ranging from 585 to 625 nm, significant for the "Yellowness" of vegetation both on land and water; 3. a RED EDGE band, ranging from 705 to 745 nm, strategically centered at the onset of the high reflectivity portion of vegetation response so potentially significant in the measurement of plant health; 4. a longer wavelength NEAR INFRARED band (NIR2), ranging from 860 to 1040 nm, partially overlapping the NIR1 band and sensitive to atmospheric water vapor absorption.
In literature, many studies deal with the use of the add on bands of the Worldview-2 sensor with respect to the traditional bands of the most common commercial satellites searching for new indexes in different application fields such as bathymetry [1], or vegetation and agricultural purposes ( [2], [3]). In [4] the authors analyze the high correlation among some bands of the Worldview-2, like COASTAL and BLUE bands or NIR1 and NIR2 bands which could mean redundant and useless information associated with some of the add on bands.
The aim of this work is the study of the performance of the whole spectral information offered by the Worldview-2 sensor for the characterization and the classification of some selected land cover targets. Three main land cover targets were recognized: "Water", "Bare lands" and "Vegetated lands". The Worldview-2 image was, firstly, used for a finer discrimination of different sub-classes on the ground belonging to the land cover targets with the application of an unsupervised approach and the help of a certified CORINE-like Land Use Map, at a 1:10.000 scale. A hyperspectral image acquired by the airborne MIVIS sensor was used to analyze the spectral profiles characterizing each distinct sub-class. Then a standard Maximum Likelihood classifier was applied to the Worldview-2 image with different input configurations as below: 1. the 4 bands (R,G,B,NIR1) common to the standard commercial multispectral sensors at very high spatial resolution; 2. the 4 bands R,G,B,NIR1 adding on, one at a time, the new bands; 3. the new complete configuration with 8 spectral bands.
The accuracy of the classification map was estimated using a set of test fields randomly selected on the ground truth map.
ITT ENVI© and GRASS software were used to analyze and process data.

The Worldview-2 data
The data set analyzed was a Worldview-2 image granted by DigitalGlobe over an area of 100 km 2 chosen by the authors among the available archive acquisitions. The scene, acquired on the 13th June 2010, includes the region known as the "Natural Oasis of Lago Salso", an area essentially wet and marshy, sited in the south-east of the Capitanata in the Apulia Region, Italy. The Natural Oasis of Lago Salso is characterized by the presence of a wetland of considerable importance (one of the most important in southern Italy) as a breeding and step birds station. The area falls in the Natura 2000 network, found within the boundaries of the Site of Community Interest (SCI) IT 9110005 "Zone umide della Capitanata" and of the Special Protection Area (SPA) IT9110038 "Paludi presso il Golfo di Manfredonia". The Natural Oasis of Lago Salso falls also within the Gargano National Park. The Natural Oasis has an extent of about 1040 ha and only 500 ha are wetland "sensu strictu", the remaining part is covered by cultivated or partially abandoned areas. Agricultural areas cover a wide surface formerly occupied by coastal lagoons (until the 1950s) and subsequently buried and used for agricultural purposes. SCI and SPA have an extent, respectively, of 14,109 ha and 14,437 ha. Water bodies are subject to fluctuations of water levels over the year, creating ecological gradients due to the variation of salt rates and moisture in soil. Soil salinity gradually increases with soil elevation, reaching a maximum just above mean high sea level (MHSL). Above the MHSL, the salinity tends to decrease due to progressively less frequent flooding. The zonation of the vegetation of salt marshes is typically associated with the tolerance to these ecological gradients.

Preprocessing
The image was calibrated in order to produce the reflectance image and to obtain the spectral profiles of some targets in the scene to compare with a previously acquired hyperspectral data set. The processing includes the following steps: 1. transformation of digital numbers into the spectral radiance values at TOA (Top Of Atmosphere). This first calibration step, known as absolute radiometric calibration, consists of multiplying radiometrically corrected image pixels by the appropriate absolute radiometric calibration factor to get band-integrated radiance (W/m 2 ·sr) and then dividing the result by the appropriate effective bandwidth to get spectral radiance (W/m 2 ·sr·μm). The absolute radiometric calibration factor and effective bandwidths are delivered with the image and available in the image metadata files (extension .IMD); 2. transformation of TOA spectral radiance into TOA reflectance. TOA reflectance is defined as the ratio of radiance reflected from a surface target to the solar irradiance incident on the surface. It is obtained using the formula: where θ S is the Solar Zenith Angle, L is the spectral radiance for a defined pixel and wavelength and Es is the mean solar spectral irradiance. The term d ES is the earth-sun distance in astronomical units as a function of the viewing day and time.

Selection and characterization of targets
An existing certified CORINE-like land use map at 1:10000 scale [5] was considered as ground truth. The map was produced in 2006. It originally showed a set of 40 land use thematic classes: after a first screening only land cover classes were selected. An unsupervised analysis was used to cluster the EO data into a certain number of spectrally different signatures. To accomplish this task the "K-Means" algorithm was considered and the 8 bands of the Worldview-2 image were used as input. After a few attempts, a number of 20 unlabelled classes (with a maximum number of 50 iterations until the convergence and 1% as the change threshold to end the iterative process) were selected. Comparing the clusters with the ground truth information resulted in the splitting/merging of certain classes, for a total number of 18 land cover classes. As shown in Figure 2 where a bathymetry map of the test site is represented, the 8 band segmented map reports 3 differentiated clusters in correspondence with the ground truth class labeled as "Sea". These three different signatures could be associated with different depth values of the sea. The 18 selected classes, grouped into 4 main land cover targets of interest were "Water", "Bare lands", "Vegetated lands" and "Artificial", as shown in Table 1. The target "Artificial" was eliminated due to its poor presence in the scene. A sample of each considered class is shown in Figure 3.  Table 1. Different land cover classes selected for supervised classification.

Analysis of the targets' spectral profiles
The analysis of the targets' spectral profiles was carried out by means of a dataset composed by an  Table 2.
www.intechopen.com  Because of its flexible airborne platform for remote sensing, the MIVIS system is able to acquire images with a good spatial resolution. The MIVIS acquisition used for this analysis was made at a height of 1.5 Km so the spatial resolution is 3 m at nadir. In order to compare MIVIS spectra with those produced by Worldview-2, the pixel values of MIVIS images were also converted into reflectance.
Due to the unknown quality of the MIVIS data pre-processing calibration, the comparison between the Worldview-2 and the MIVIS profiles has to be considered in terms of spectral profile trends. In addition, no atmospheric correction was made to both the images [6]. As a consequence, the consideration that the atmosphere contributes in a different way to the reflectance measured by sensors, due to the different day of acquisition and the different flight height of sensors, should be observed. In Figure 4, a subset of the MIVIS acquisition corresponding to the Worldview-2 image is shown. Close to the right edge of the frame a slight pattern of sunglint is visible. It is presumed that it influences the reflectance of the sea.
The spectral analysis was carried out by selecting regions which can be considered representative of the 18 classes identified by the unsupervised analysis and the ground truth map. A time interval of about one year between the MIVIS and Worldview-2 acquisitions restricts the selection of target areas to regions not affected by significant changes between the two dates. In fact there could be some arable lands where crops changed for agricultural practices or were covered with screening covers. Moreover, it should be considered that the two images were acquired in two different spring months corresponding to the different phenological status of the same crop. In Figure 5, Worldview-2 (top) and MIVIS (bottom) water target spectral profiles are shown. As it can be noticed for all the target profiles, there is an atmospheric contribution to the Worldview-2 reflectance. In particular, in the range of shorter wavelengths of the COASTAL and the BLUE bands, the Rayleigh scattering is the prevailing contribution while for the longer wavelengths, like the NIR2 band, the water vapor absorption is dominant. The gaseous absorption results as visible also for the MIVIS profiles. It can be noted that: -"Sea Water 1", "Sea Water 2" and "Sea Water 3" correspond to: deep water (far from the coast), intermediate deep water and coastal water, respectively. The Worldview-2 reflectance profiles, which tend to converge at shorter wavelengths due to atmospheric effects, show increasing reflectance values for sea regions closer to the coast due to the increasing contribution of suspended sediments [7]; -"Sea Water 3" and "River Water" MIVIS spectral profiles are characterized by particulate material and/or a land-derived yellow substance which can influence the reflectance [8]; with regard to the "Marsh Water" classified regions, they are relative to a series of waterbodies alternating with different kinds of vegetated areas belonging to the Natural Oasis of Lago Salso ( Figure 6). Specifically, it encompasses three waterbodies with depths ranging from 50 to 170 cm, depending on the seasonal level and the regional operational necessities which are fed by a small river. This class shows a spectral profile which starts to be strongly affected by the bottom vegetation contribution. In the NIR the reflectance is much higher than that in the visible band due to the cellular structure in the leaves. The slope of the spectrum profile between RED and NIR is characteristic of the vegetation species and gives information about plant health [9]. Spectral profiles also show a reduction in band 20 (Table 1) due to atmospheric absorption. Analyzing MIVIS spectra some considerations about vegetated land classes can be made. The class labeled as "Arable Land with Vegetation 2" shows a reduced increase of reflectance in the wavelength range between RED and NIR and a peak of reflectance in correspondence with the GREEN range which is less evident with respect to the other profiles. Considering the particular color of the regions and the presence of an almost regular texture, it is possible that this class is related to arable fields covered by a thick net typical of local agricultural practices.
Classes labeled as "Vegetated Marshy Areas 1" and "Vegetated Marshy Areas 2" are relative to different kinds of vegetation characterizing the "Natural Oasis of Lago Salso" (Figure 6). The Worldview-2 profiles, due to atmospheric effects, do not show the typical trend of vegetation spectra in the visible range. The absorption peak in the BLUE range is suppressed by the Rayleigh scattering contribution which decreases with an increase in wavelength. On the contrary, the range of the spectrum from RED to NIR1 can be useful for vegetation characterization and, except for a few differences (which could be explained considering the time interval between the two acquisitions) MIVIS and Worldview-2 spectral profiles are sufficiently in agreement. The class labeled as "Forested Area "is mainly composed of the Siponto pine forest (Figure 8) sited in a coastal area on the Manfredonia Gulf. The spectral profile obtained by MIVIS and confirmed by Worldview-2 shows a low reflectance in the NIR spectral range which is correlated to lower vegetation LAI [10].
In Figure 9, Worldview-2 (top) and MIVIS (bottom) spectral profiles for bare land regions are shown. In this case the trend of MIVIS spectral profiles are in agreement with the Worldview-2 ones; although, the better spectral resolution of MIVIS is able to acquire finer spectral signatures for every class.  It can be noted that "Arable Land without Vegetation 1", "Arable Land without Vegetation 2" and "Arable Land without Vegetation 3" are characterized by a spectral signature similar in shape but with an increasing average reflectance. This can be explained considering that the reflectance level decreases for soil with increasing moisture [11] and so the different targets could be associated with different moisture content. "Sand", instead, shows a spectral profile which differs from the other ones probably due to the extremely different composition of the soil.

Processing, results and discussion
For the supervised analysis, the standard statistic "Maximum Likelihood" (ML) algorithm was considered. The 18 classes of table 1, recognized on the scene with the guide of the segmentation step and better characterized with the help of MIVIS data, were selected. Randomly selected training (TR) and test (TE) sets were used respectively to train the algorithm and to assess the accuracy of the produced maps. For the accuracy of all the classes, the Overall Accuracy percentage (OA%) (i.e. number of correctly classified pixels divided by the total of pixels) with the estimation of the relative confidence interval with a significance of 95% [12] as computed. For the accuracy of each class, the Mapping Accuracy percentage (MA%), [13], [14], was computed. It is defined as: where: pixels omission is the number of pixels assigned to other classes along the row of the confusion matrix relevant to the class considered; pixels commission is the number of pixels assigned to other classes along the column of the confusion matrix relevant to the class considered.
According to [15], many input configurations to the classifier were tested considering, firstly, the standard 4 spectral bands of the image and then adding a fifth band among the 4 add on bands of Worldview-2 in order to analyze the specific contribution of each band.  Table 3. Results in the supervised classification.
In training and testing, with an increase in the number of the bands there is an increase in the OA% because more information was added as input to the classifier to improve discrimination among classes. Observing the generalization ability, in testing, an improvement of 10% was achieved with the use of 8 bands with respect to 4 bands. The asterisk indicates the best value.
The same analysis was carried out for each target ("Water"-"Bare land"-"Vegetated land") in order to evaluate the contribution that each of the add on 4 bands could give to characterize the specific target. A finer detailed discrimination among the classes is expected. For the target "Water" the MA% in testing in the different input configurations to the classifier are shown in Table 4.  Table 4. MA% in test for the classes belonged to the target Water.
The best MA% value is obtained with the use of 8 bands, as indicated with a double asterisk, with an average improvement of 20% with respect to the use of only 4 bands. Analyzing the contribution of each add on band to the single class, it emerged that (the best value due to the add on bands has been marked with a single asterisk):  the discrimination of "Sea Water 1" (deep) and "Marsh Water" is improved by the NIR2 band. "Marsh Water" is water with the presence of vegetation under and over the surface and this could explain the role of NIR2; www.intechopen.com  the discrimination of "Sea Water2 " (medium deep) and "Sea Water 3" (coastal) is improved by the YELLOW band that appears to be able to recognize water with hanging deposits;  the discrimination of "River Water", substantially muddy water, is improved by the COASTAL band which appears able to recognize a mixture of water and mud.
For the target "Bare land", the MA% in testing in the different input configurations to the classifier are shown in Table 5. For all the different spectral signatures, the best MA% value is obtained with the use of 8 bands, as indicated with a double asterisk, with an average improvement of 10% with respect to the use of only 4 bands. The class "Arable Land without Vegetation 4" is an exception which can be justified by a high misclassification with the class "Arable Land without Vegetation 3". Analyzing the contribution of each add on band to the single class, it emerged that (the best value due to the add on bands has been marked with a single asterisk):  the discrimination of "Arable Land without Vegetation 1" (dark brown) and "Arable Land without Vegetation 2" (light brown) is improved by the YELLOW band;  the discrimination of "Arable Land without Vegetation 3" (orange) is improved by the NIR2 band and by the RED EDGE, whereas "Arable Land without Vegetation 4" (very light brown) is improved only by the NIR2 band;  the discrimination of "Sand" is improved by the COASTAL band.
The different spectral profiles could be explained by the different pedological composition of soil or its different water content. For the "Vegetated land" target, the MA% test classification values obtained with different input bands are shown in Table 6.
The best MA% value is obtained with 8 bands, as evidenced by a double asterisk in the table, with an average improvement of about 3% for "Forested Area" and "Vegetated Marshy Areas" and of about 30% for "Arable Land with Vegetation 1" and "Arable Land with Vegetation 2" with respect to the use of only 4 bands. For each class, the best result obtained by a specific band is evidenced by a single asterisk in the

Conclusions
This paper describes the experimental activity aimed at the exploitation of the new Worldview-2 sensor with respect to the effectiveness of the new add on COASTAL, YELLOW, RED EDGE and NIR2 bands. Firstly, an unsupervised analysis for data spectral clustering was applied to discriminate among the different spectral signatures, then a supervised image classification produced a land cover map. Standard/commercial tools were used. In the first step the clusters in the spectral domain were interpreted with the help of a detailed ground truth map and compared with a hyperspectral data set. This analysis showed that the 8-band sensor is extremely useful to better discriminate different spectral sub-signatures corresponding to the same land cover category. This means that the major capability of the new sensor resides in the capacity of investigating the "ground" diversity underlying the apparent homogeneity of conventional land cover/land use map categorization. From the supervised classification, it was possible to detect changes in the bathymetry for the "Sea Water" classes by using the COASTAL band; moreover, the lowest wavelength band appears to be significant for the recognition of mixed patterns of water and terrain. The YELLOW band appears significant to detect the presence of hanging deposits or to elicit terrain composition, as characterized by a certain degree of "yellowness". Finally, the RED EDGE and the NIR2 bands seem useful for a better discrimination of ground sites characterized by a mixing of water and vegetation. The increase in thematic accuracy was 10%, passing from the "traditional" 4-band to the new 8band sensor. www.intechopen.com

Acknowledgements
This work was supported by the project "Flight Risks Mitigation and Nowcasting at Airports" (RIVONA) funded by the Apulia Region, POFESR 2007-2013. Today, space technology is used as an excellent instrument for Earth observation applications. Data is collected using satellites and other available platforms for remote sensing. Remote sensing data collection detects a wide range of electromagnetic energy which is emitting, transmitting, or reflecting from the Earth's surface. Appropriate detection systems are needed to implement further data processing. Space technology has been found to be a successful application for studying climate change, as current and past data can be dynamically compared. This book presents different aspects of climate change and discusses space technology applications.

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