Gaofen-3 PolSAR Image Classification via XGBoost and Polarimetric Spatial Information.

The launch of the Chinese Gaofen-3 (GF-3) satellite will provide enough synthetic aperture radar (SAR) images with different imaging modes for land cover classification and other potential usages in the next few years. This paper aims to propose an efficient and practical classification framework for a GF-3 polarimetric SAR (PolSAR) image. The proposed classification framework consists of four simple parts including polarimetric feature extraction and stacking, the initial classification via XGBoost, superpixels generation by statistical region merging (SRM) based on Pauli RGB image, and a post-processing step to determine the label of a superpixel by modified majority voting. Fast initial classification via XGBoost and the incorporation of spatial information via a post-processing step through superpixel-based modified majority voting would potentially make the method efficient in practical use. Preliminary experimental results on real GF-3 PolSAR images and the AIRSAR Flevoland data set validate the efficacy and efficiency of the proposed classification framework. The results demonstrate that the quality of GF-3 PolSAR data is adequate enough for classification purpose. The results also show that the incorporation of spatial information is important for overall performance improvement.