MULTISOURCE CROSS-SCENE CLASSIFICATION USING FRACTIONAL FUSION AND SPATIAL-SPECTRAL DOMAIN ADAPTATION

Abstract

To solve the limitation of labeled samples in hyperspectral image (HSI) classification, cross-scene learning methods are developed recently. However, the disparity caused by environmental variation between HSI scenes is still a challenge. As a supplement, light detection and ranging (LiDAR) data provides elevation and spatial information regardless the variations. In this paper, we propose a multisource cross-scene classification method using fractional fusion and spatial-spectral domain adaptation to reduce disparity between scenes. The spatial information of HSI is preserved by fractional differential masks (FrDM) firstly. Then the LiDAR data is utilized for spectral alignment of HSI. The utilization of LiDAR data reduces the pixel-level disparity between scenes. At last, a spatial-spectral domain adaptation network is proposed for feature extraction and classification. Experimental results on HSI and LiDAR scenes show 5% improvements in overall accuracy compared with state-of-the-art methods.