Multi-Source Remote Sensing Data Cross Scene Classification Based on Multi-Graph Matching

Multi-source joint classification has been extensively investigated in single scenario setting; however, for cross scene (CS) classification, few studies have been conducted for evaluating the collaborative performance of multi-sources. In this paper, using hyperspectral image (HSI) and light detection and ranging (LiDAR) data, we propose a multi-source CS classification method, and build source-related alignment to reduce statistical shift. Both geometrical and statistical alignments are considered to learn common-subspaces of each source with preserving discrimination information. Finally, the aligned features from both sources are integrated for final classification. Experimental results demonstrate the superior of the proposed method over other state-of-the-art CS approaches.