Abstract
Abstract. Land use land cover classification is one of the widely used applications in the field of remote sensing. Accurate land use land cover maps derived from remotely sensed data is a requirement for analyzing many socio-ecological concerns. The present study investigates the capabilities of dual polarimetric C-band SAR data for land use land cover classification. The MRS mode level 1 product of RISAT-1 with dual polarization (HH & HV) covering a part of Varanasi district, Uttar Pradesh, India is analyzed for classifying various land features. In order to increase the amount of information in dual-polarized SAR data, a band HH + HV is introduced to make use of the original two polarizations. Transformed Divergence (TD) procedure for class separability analysis is performed to evaluate the quality of the statistics prior to image classification. For most of the class pairs the TD values are greater than 1.9 which indicates that the classes have good separability. Non-parametric classifier Support Vector Machine (SVM) is used to classify RISAT-1 data with optimized polarization combination into five land use land cover classes like urban land, agricultural land, fallow land, vegetation and water bodies. The overall classification accuracy achieved by SVM is 95.23 % with Kappa coefficient 0.9350.
Highlights
The accurate and timely land use land cover (LULC) information is essential for analyzing many socio-ecological concerns
The Geocoding is performed by using leader file information of the data and the digital number (DN) values of C-band RISAT-1 data is converted into backscattering values in decibel unit for HH and HV polarization
The class separability analysis is performed for the polarization combination used for the classification
Summary
The accurate and timely land use land cover (LULC) information is essential for analyzing many socio-ecological concerns. Remote sensing data obtained from various optical sensors have been frequently used to derive LULC information (Saatchi et al, 1997; Roberts et al, 2003; Thenkabail et al, 2005). The conventional optical remote sensing is inadequate because of weather conditions. The difficulties are encountered in collecting timely LULC information. Microwave remote sensing have the capability of penetrating through the clouds overcoming the atmospheric effects and is an effective tool for extracting timely LULC information. Earlier the space shuttle SIR-C/X-SAR data has been mainly used to investigate LULC information (Saatchi et al, 1997; Pierce et al, 1998)
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