Instance-wise Domain Generalization for Cross-scene Wetland Classification with Hyperspectral and LiDAR Data

Wetland is one of the three ecosystems in the world, and collaborative monitoring using hyperspectral images (HSIs) and light detection and ranging (LiDAR) has been important for wetland ecological protection. However, because of the domain shift of different images, cross-scene wetland classification of HSI and LiDAR is a practical challenge, necessitating the development of models trained solely on the source domain (SD), and directly transferred to the target domain (TD) without retraining. To address this issue, an instance-wise domain generalization network (IDGnet) is proposed for HSI and LiDAR cross-scene wetland classification. An instance-wise random domain expansion module is developed to simulate the domain shift, establishing the extended domain (ED). Specifically, the original HSI and LiDAR data are separated as semantic and background information in the frequency domain, a random background shift is applied to the HSI, and a semantic random shift is deployed to LiDAR. The HSI and LiDAR fusion features are extracted from the SD and ED by a weight-shared network. Multiple condition constraints are proposed for domain and class alignment, learning the domain-invariant and class-specific information, and improving model generalization. Experiments conducted on two wetlands datasets demonstrate the superiority of the proposed IDGnet for cross-scene wetland classification with HSI and LiDAR data. The codes will be available from the website: https://github.com/bigshot-g/IEEE_TGRS_IDGnet.