Exploring the Performance of Different Texture Information and Polarization Features from PolSAR Images in Urban Land Cover Classification

Abstract

With the improvement of spatial resolution of polarimetric synthetic aperture radar (PolSAR ) images, there was more research on texture features extracted from PolSAR images; however, the performance of texture features derived from different descriptors in PolSAR images was not fully explored. In this study, the performance of combining polarization features with texture features extracted by four descriptors, respectively, in land cover classification was explored. The experimental results indicated that: (1) different texture features have different abilities to distinguish ground objects; (2) with the improvement of spatial resolution of PolSAR images, texture features should not be ignored; (3) polarization features are very important for land cover classification, texture features can be used as auxiliary features to further improve the classification accuracy. This study could serve as a meaningful reference for information extraction from PolSAR images.