Land Cover Classification Using Radiometric-Terrain-Calibrated Polarimetric Sar Images

The radiometric quality of polarimetric SAR (PolSAR)/SAR images is affected by terrain undulations, and the resultant radiometric distortions should be calibrated to facilitate quantitative applications as land cover classification. This paper presents a terrain-related radiometric calibration method to a quad-polarimetric Advanced Land Observing Satellite phased array type L-band synthetic aperture radar (ALOS PALSAR) image. A digital elevation model (DEM) was used for accurate detection of layover and shadow areas. Precise calibration was done subsequently. Polarimetric features were extracted and a supervised random forest (RF) classifier was then employed. Five classes were extracted as waterbody, bare soil, farmland, forest, and man-made objects. Accuracy assessment was performed and the results were analyzed. Improvement of overall accuracy from 78.67% to 82.67% and that of kappa coefficient from 0.73 to 0.78 was achieved using the radiometricterrain-calibrated (RTC) features, which shows great necessity of RTC processing for PolSAR land cover classification in mountainous areas.