Cross-Domain Classification of Multisource Remote Sensing Data Using Fractional Fusion and Spatial-Spectral Domain Adaptation

Abstract

Limitation of labeled samples has always been a challenge for hyperspectral image (HSI) classification. In real remote sensing applications, we encounter a situation where an HSI scene is not labeled at all. To solve this problem, crossdomain learning methods are developed by utilizing another HSI scene with similar land covers and sufficient labeled samples. However, the disparity between HSI scenes is still a challenge in reducing the classification performance, which may be affected by variations in illumination and weather. As a robust supplement to these variations, light detection and ranging (LiDAR) data provides stable elevation and spatial information. In this paper, we propose a multisource cross-domain classification method using fractional fusion and spatial-spectral domain adaptation to reduce the disparity between scenes. The spatial information of HSI is preserved by fractional differential masks (FrDM) first. 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 (SSDA) is proposed to reduce domain shift at the feature level and extract discriminative spatial-spectral features. Experimental results on HSI and LiDAR scenes show 5% − 10% improvements in overall accuracy compared with state-of-the-art methods.

Deep Learning Methods for Semantic Segmentation in Remote Sensing with Small Data: A Survey

“…The differences in the classes could be shared between the two domains while detecting unknown classes in the target domain. Zhao et al [222] proposed the incorporation of LiDAR data using GAN networks to decrease the discrepancy between the source and target domains by reducing the per-pixel spectral shift. They also utilized a modified fractional differential mask (FrDM) method to extract spatial spectral information.…”