Land Cover Semantic Segmentation of High-Resolution Gaofen-3 SAR Image

Abstract

Land cover classification with SAR images mainly focuses on the utilization of fully polarimetric SAR (PolSAR) images. This paper explores the potential of semantic segmentation of high-resolution single polarimetric (single-pol) SAR and PolSAR images, in particular tailored for the Gaofen-3 (GF-3) sensor. First of all, a unified SAR data preprocessing method is utilized to deal with the L2 format SAR data. Then, an encoder-decoder network based on transfer learning is designed to implement semantic segmentation of GF-3 SAR images. Experiments on single-pol SAR and PolSAR images demonstrate the feasibility of semantic segmentation with high-resolution GF-3 images.