Remote-Sensing Scene Classification via Multistage Self-Guided Separation Network

Abstract

In recent years, remote sensing scene classification is one of research hotspots and has played an important role in the field of intelligent interpretation of remote sensing data. However, various complex objects and backgrounds form a variety of remote sensing scenes through spatial combination and correlation, which brings great challenges to accurately classify different scenes. Among them, the insufficient feature difference brought about the unbalanced change of background and target between inter-class sample and the feature representation inconsistency caused by the difference of representation among the intra-class samples have become obstacles to effectively distinguish different scene images. To address these issues, a Multi-stage Self-Guided Separation Network (MGSNet) is propo