Development of polarimetric decomposition techniques for land use and land cover mapping using RISAT-1 radar satellite sensor data

Abstract

In this paper, we are examines polarimetric decomposition techniques like on Pauli decomposition and Sphere Di-Plane Helix (SDH) decomposition of RISAT-1 satellite image for land use and land cover mapping. The data processing methods adopted are 1) Pre-processing, antenna pattern correction, sigma nought calibration, Speckle Reduction, 2) Polarimetric Decomposition and 3) Polarimetric Classification. We have used RISAT-1 satellite image datasets of Mysore-Mandya region of Karnataka, India for classifying five classes - agricultural lands, urban area, forest land, water land and barren land. Polarimetric SAR data possess a high potential because it captures earth land surface features. After applying the polarimetric classification techniques, post-classification techniques is applied in order to access the classification accuracy. The Post-classification step gives the over-all accuracy was observed to be higher in the SDH decomposed image, as it operates on individual pixels on a coherent basis and utilises the complete intrinsic coherent nature of polarimetric SAR data as compared to the ground truth collected through GPS measurements and maps. Thereby, making SDH decomposition particularly suited for analysis of high-resolution SAR data. The Pauli Decomposition represents all the polarimetric information in a single SAR image however interpretation of the resulting image in less accuracy. The SDH decomposition technique seems to produce better results and interpretation as compared to Pauli Decomposition however more quantification and further analysis are being done in this area of research. The comparison of the Polarimetric decomposition techniques will be the scope of the paper. Polarimetric decomposition techniques helps in better understanding earth land surface features.