The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XL-8
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 833–837, 2014
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 833–837, 2014

  28 Nov 2014

28 Nov 2014

Classification of various land features using RISAT-1 dual polarimetric data

V. N. Mishra, P. Kumar, D. K. Gupta, and R. Prasad V. N. Mishra et al.
  • Department of Physics,Indian Institute of Technology (BHU), Varanasi, India

Keywords: Land use land cover, Dual Polarimetric, RISAT-1, Transformed Divergence, SVM

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.