Shridhar D. Jawak

Improved land cover mapping using high resolution multiangle 8-band WorldView-2 satellite remote sensing data

Abstract Here, we discuss the improvements in urban classification that were made using the spatial-spectral-angular information from a WorldView-2 (WV-2) multiangle image sequence. In this study, we evaluate the use of multiangle high resolution WV-2 panchromatic (PAN) and multispectral image (MSI) data for extracting urban geospatial information. Current multiangular WV-2 data were classified into misclassification-prone surfaces, such as vegetation, water bodies, and man-made features, using a cluster of normalized difference spectral index ratios (SIR). A novel multifold methodology protocol was designed to estimate the consequences of multiangularity and germane PAN-sharpening algorithms on the spectral characteristics (distortions) of satellite data and on the resulting land use/land cover (LU/LC) mapping using an array of SIRs. Eight existing PAN-sharpening algorithms were used for data fusion, followed by estimation of multiple SIRs to mitigate spectral distortions arising from the multiangularity of the data. This research highlights the benefits of using traditional PAN-sharpening techniques with a specific set of SIRs on land cover mapping based on five available tiles of satellite data. The research provides a method to overcome the atmospherically triggered spectral distortions of multiangular acquisitions, which will facilitate better mapping and understanding of the earth’s surface.

A Semiautomatic Extraction of Antarctic Lake Features Using Worldview-2 Imagery

We devised a semiautomatic approach for extracting lake features based on a customized set of normalized difference water index (NDWI) information which was obtained by incorporating high resolution, 8-band WorldView-2 data. An extensive accuracy assessment was carried out for three semiautomatic feature extraction approaches for extracting 36 lake features on Larsemann Hills, Antarctica. The method was tested on five existing PAN-sharpening algorithms, which suggest that the customized NDWI approach renders intermediate performance (root mean square error varies from ~227 to ~235 m 2 ) and highest stability when compared with existing feature extraction techniques. In general, the customized NDWI rendered a least misclassification ( ≈11 percent), followed by target detection (≈16 percent) and spectral processing (≈17 percent) methods for extraction of 36 lakes. We also found that customized NDWI caused consistently least misclassification ( ≈21 percent) than the target detection (≈23 percent) and spectral processing (≈30 percent) methods for extraction of partially snow or ice-covered 11 lakes. Our results indicate that the use of the customized NDWI approach and appropriate PANsharpening algorithm can greatly improve the semiautomatic extraction of lake features in cryospheric environment.

Very high-resolution satellite data for improved land cover extraction of Larsemann Hills, Eastern Antarctica

Abstract We compared four different image classification methods to improve the accuracy of cryospheric land cover mapping from very high-resolution WorldView-2 (WV-2) satellite images. We used four pixel-by-pixel classification methods and then integrated the classified images using a winner-takes-all (WTA) approach. The images on which we performed the classification techniques were made up of eight-band multispectral images and panchromatic WV-2 images fused using the hyperspherical color sharpening method. We used four distinctly different methods to classify the WV-2 PAN-sharpened data: a support vector machine (SVM), a maximum likelihood classifier (MXL), a neural network classifier (NNC), and a spectral angle mapper (SAM). Three classes of land cover—land mass/rocks, water/lakes, and snow/ice—were classified using identical training samples. The final thematic land cover map of Larsemann Hills, east Antarctica, was integrated using ensemble classification based on a majority voting–coupled WTA method. Results indicate that the WTA integration method and the SVM classification method were more accurate than the MXL, NNC, and SAM classification methods. The overall accuracy of the WTA method was 97.23% (96.47% with the SVM classifier) with a 0.96 kappa coefficient (0.95 with the SVM classifier). The accuracy of the other classifiers were 93.73 to 95.55% with kappa coefficients of 0.91 to 0.93. This work demonstrates the strengths of different classifiers to extract land cover information from multispectral data collected in cryospheric regions.

High-resolution multispectral satellite imagery for extracting bathymetric information of Antarctic shallow lakes

High-resolution pansharpened images from WorldView-2 were used for bathymetric mapping around Larsemann Hills and Schirmacher oasis, east Antarctica. We digitized the lake features in which all the lakes from both the study areas were manually extracted. In order to extract the bathymetry values from multispectral imagery we used two different models: (a) Stumpf model and (b) Lyzenga model. Multiband image combinations were used to improve the results of bathymetric information extraction. The derived depths were validated against the in-situ measurements and root mean square error (RMSE) was computed. We also quantified the error between in-situ and satellite-estimated lake depth values. Our results indicated a high correlation (R = 0.60~0.80) between estimated depth and in-situ depth measurements, with RMSE ranging from 0.10 to 1.30 m. This study suggests that the coastal blue band in the WV-2 imagery could retrieve accurate bathymetry information compared to other bands. To test the effect of size and dimension of lake on bathymetry retrieval, we distributed all the lakes on the basis of size and depth (reference data), as some of the lakes were open, some were semi frozen and others were completely frozen. Several tests were performed on open lakes on the basis of size and depth. Based on depth, very shallow lakes provided better correlation (≈ 0.89) compared to shallow (≈ 0.67) and deep lakes (≈ 0.48). Based on size, large lakes yielded better correlation in comparison to medium and small lakes.

Exploratory normalized difference water indices for semi-automated extraction of Antarctic lake features

This work presents various normalized difference water indices (NDWI) to delineate lakes from Schirmacher Oasis, East Antarctica, by using a very high resolution WorldView-2 (WV-2) satellite imagery. Schirmacher oasis region hosts a number of fresh as well as saline water lakes, such as epishelf lakes, ice-free or landlocked lakes, which are completely frozen or semi-frozen and in a ice-free state. Hence, detecting all these types of lakes distinctly on satellite imagery was the major challenge, as the spectral characteristics of various types of lakes were identical to the other land cover targets. Multiband spectral index pixel-based approach is most experimented and recently growing technique because of its unbeatable advantages such as its simplicity and comparatively lesser amount of processing-time. In present study, semiautomatic extraction of lakes in cryospheric region was carried out by designing specific spectral indices. The study utilized number of existing spectral indices to extract lakes but none could deliver satisfactory results and hence we modified NDWI. The potentials of newly added bands in WV-2 satellite imagery was explored by developing spectral indices comprising of Yellow (585 – 625 nm) band, in combination with Blue (450 – 510 nm), Coastal (400 – 450 nm) and Green (510 – 580 nm) bands. For extraction of frozen lakes, use of Yellow (585 – 625 nm) and near-infrared 2 (NIR2) band pair, and Yellow and Green band pair worked well, whereas for ice-free lakes extraction, a combination of Blue and Coastal band yielded appreciable results, when compared with manually digitized data. The results suggest that the modified NDWI approach rendered bias error varying from ~1 to ~34 m2.

Semi-automatic extraction of supra-glacial features using fuzzy logic approach for object-oriented classification on WorldView-2 imagery

High resolution satellite data provide high spatial, spectral and contextual information. Spatial and contextual information of image objects are in demand to extract the information from high resolution satellite data. The supraglacial environment includes several features that are present on the surface of the glacier. The extraction of features from supraglacial environment is quite challenging using pixel-based image analysis. To overcome this, objectoriented approach is implemented. This paper aims at the extraction of geo-information from the supraglacial environment from high resolution satellite image by object-oriented image analysis using the fuzzy logic approach. The object-oriented image analysis involves the multiresolution segmentation for the creation of objects followed by the classification of objects using the fuzzy logic approach. The multiresolution segmentation is executed on the pixel level initially which merges pixels for the creation of objects thus minimizing their heterogeneity. This is followed by the development of rule sets for the classification of various features such as blue ice, debris, snow from the supraglacial environment in WorldView-2 data. The area of extracted feature is compared with the reference data and misclassified area of each feature using various bands is determined. The present object oriented classification achieved an overall accuracy of ≈ 92% for classifying supraglacial features. Finally, it is suggested that Red band is quite effective in the extraction of blue ice and snow, while NIR1 band is effective in debris extraction.

Multisource classification and pattern recognition methods for polar geospatial information mining using WorldView-2 data

Current research study emphasizes the importance of advanced digital image processing methods in order to delineate between various LULC features. In the case of the Antarctica, the present LC (snow/ice, landmass, water, vegetation etc.) and the present LU (research stations of various nations) needs to be mapped accurately for the hassle free routine activities. Geo-location has become the most important part of geosciences studies. In this paper we have tried to locate three most important features (snow/ice, landmass, and water) and also have extracted the extent of the same using the multisource classification (image fusion/pansharpening) and pattern recognition (supervised/unsupervised methods, index ratio methods). Innovation in developing spectral index ratios has led us to come up with an unique ratio named Normalized Difference Landmass Index (NDLI) which performed better (Avg. Bias: 51.99m) than other ratios such as Normalized Difference Snow/Ice Index (NDSII) (Avg. Bias: -1572.11m) and Normalized Difference Water Index (NDWI) (Avg. Bias: 1886.60m). The practiced trial and error methodology quantifies the productivity of not only the classification methods over one other but also that of the fusion methods. In present study, classifiers used (Mahalanobis and Winner Takes All) performed better (Avg. Bias: 122.16 m) than spectral index ratios (Avg. Bias: 620.16 m). The study also revealed that newly introduced bands in WorldView-2, band 1 (Coastal Blue), 4 (Yellow), 6 (Red-edge) and 8 (Near Infrared-2) along with traditional bands have the capacity to mine the polar geospatial information with utmost accuracy and efficiency.

Geospatial mapping of Antarctic coastal oasis using geographic object-based image analysis and high resolution satellite imagery

An accurate spatial mapping and characterization of land cover features in cryospheric regions is an essential procedure for many geoscientific studies. A novel semi-automated method was devised by coupling spectral index ratios (SIRs) and geographic object-based image analysis (OBIA) to extract cryospheric geospatial information from very high resolution WorldView 2 (WV-2) satellite imagery. The present study addresses development of multiple rule sets for OBIA-based classification of WV-2 imagery to accurately extract land cover features in the Larsemann Hills, east Antarctica. Multilevel segmentation process was applied to WV-2 image to generate different sizes of geographic image objects corresponding to various land cover features with respect to scale parameter. Several SIRs were applied to geographic objects at different segmentation levels to classify land mass, man-made features, snow/ice, and water bodies. We focus on water body class to identify water areas at the image level, considering their uneven appearance on landmass and ice. The results illustrated that synergetic usage of SIRs and OBIA can provide accurate means to identify land cover classes with an overall classification accuracy of ≈97%. In conclusion, our results suggest that OBIA is a powerful tool for carrying out automatic and semiautomatic analysis for most cryospheric remote-sensing applications, and the synergetic coupling with pixel-based SIRs is found to be a superior method for mining geospatial information.

Object-oriented feature extraction approach for mapping supraglacial debris in Schirmacher Oasis using very high resolution satellite data

Supraglacial debris was mapped in the Schirmacher Oasis, east Antarctica, by using WorldView-2 (WV-2) high resolution optical remote sensing data consisting of 8-band calibrated Gram Schmidt (GS)-sharpened and atmospherically corrected WV-2 imagery. This study is a preliminary attempt to develop an object-oriented rule set to extract supraglacial debris for Antarctic region using 8-spectral band imagery. Supraglacial debris was manually digitized from the satellite imagery to generate the ground reference data. Several trials were performed using few existing traditional pixel-based classification techniques and color-texture based object-oriented classification methods to extract supraglacial debris over a small domain of the study area. Multi-level segmentation and attributes such as scale, shape, size, compactness along with spectral information from the data were used for developing the rule set. The quantitative analysis of error was carried out against the manually digitized reference data to test the practicability of our approach over the traditional pixel-based methods. Our results indicate that OBIA-based approach (overall accuracy: 93%) for extracting supraglacial debris performed better than all the traditional pixel-based methods (overall accuracy: 80−85%). The present attempt provides a comprehensive improved method for semiautomatic feature extraction in supraglacial environment and a new direction in the cryospheric research.

Application of high-resolution multispectral data for mapping blue ice areas in the Antarctic environment

High resolution calibrated PAN-sharpened images from WorldView-2 (WV-2) were used for extracting blue ice areas in Schirmacher Oasis, east Antarctica. The Schirmacher oasis extends from 70°45′ S to 70° 75′ S and 11°38′ E to 11° 38′ E. Blue ice areas represents long-term ablation. The amplitude of blue ice is lower than that of snow, because the ice surface is smoother than the latter. But the difference is not so obvious when applying automatic extraction techniques. To achieve desirable results and support comparative analysis, multiband image combinations were generated from atmospherically-corrected WV-2 data. For feature extraction process, regions of interest (ROI) were considered in which blue ice was used as target and white snow/ice appearing on the blue ice was considered as non-target. Various semiautomatic feature extraction methods, such as, target detection, mapping methods, etc, and many trials were used for extracting blue ice areas. Surface patterns of alternating snow and blue ice bands were found in east Antarctica which becomes obstacle to clearly extract blue ice feature. From the high resolution WV-2 data, reference data (digitized data) were prepared for blue ice area. By comparing reference data and extracted data, bias and root mean square (RMS) error values were calculated. Accuracy assessment was done considering the entire necessary prior results of the blue ice area. Our results indicate that the pixel-based supervised classification methods yielded an overall accuracy ranging from 82%-89% for extraction of blue ice areas.