Semi-supervised cross-domain feature fusion classification network for coastal wetland classification with hyperspectral and LiDAR data

Abstract

Multi-source remote sensing monitoring plays a crucial part in the ecological protection and restoration of coastal wetlands. However, due to the inaccessible of wetlands environment, lacking of labeled samples is a challenge in wetland classification. In this article, an unsupervised cross-domain feature fusion and supervised classification network (UF2SCN) is proposed for coastal wetland classification, which fuses hyperspectral image (HSI) and light detection and ranging (LiDAR) data. First, an unsupervised single branch end to end network is developed to get HSI and LiDAR fusion feature, in which a feature extraction model with spectral attention is deployed to obtain the average distribution characteristics of all samples, and the HSI and LiDAR data is utilized to guide the whole process. Second, a supervised classification network with spatial attention is applied to used fusion feature for classification, which uses the limited samples. Finally, a two stages training strategy is proposed to improve the ability of feature fusion. Experiments conducted on two coastal wetland datasets created by ourselves prove the validity of the proposed method on HSI and LiDAR classification for coastal wetland.

Keywords

• Coastal wetlands classification
• Hyperspectral image (HSI)
• Light detection and ranging (liDAR)
• Limited training samples