Hyperspectral Remote Sensing Object Detection via Cross-Domain Learning From Visible Images

Due to the high cost of data acquisition and annotation, it is challenging to obtain large-scale training data for hyperspectral remote sensing images (HRSIs), making it difficult to train object detectors directly. In contrast, visible-light remote sensing images (VLRSIs) are abundant and well-annotated. Hence, cross-domain object detection from VLRSI to HRSI (VLRSI2HRSI) provides a feasible solution for HRSI detection. However, existing cross-domain detection methods are developed for natural images. They neglect the shared spatial characteristics in the remote sensing scenario and fail to model the spectral properties of HRSIs. As a result, directly applying them to VLRSI2HRSI leads to significant performance degradation. To address this issue, we propose a Visible-light to Hyperspectral Cross-Domain Detection Network (VHCDN). We first observe that reconstructed residual representations in both VLRSI and HRSI suppress background and emphasize foreground objects. Based on this observation, we introduce a Residual Extracting Module (REM) to extract domain-shared spatial features. Furthermore, since objects of the same semantic class may consist of different materials and exhibit spectral variations under varying illumination, we design a Spectral Saliency Head (SSH) that models spectral uncertainty. By introducing one HRSI into the source-domain VLRSI datasets to guide the generation of spectral features, the SSH is trained with a spectral heteroscedastic loss and a spectral uncertainty loss, enabling the uncertainty learned in the labeled source domain to be stably transferred to the unlabeled target domain. Experiments on three cross-domain settings against seven representative methods demonstrate that VHCDN achieves state-of-the-art performance, while ablation studies verify the effectiveness of each proposed module.