Hunting weak targets: Mamba for weak target detection from panchromatic and hyperspectral satellite images

Hyperspectral image (HSI) is of great significance for target detection in remote sensing images because of its rich spectral information. However, HSI mostly has low spatial resolution, which limits the performance on weak target detection. In this work, a weak target detection Mamba based on the fusion of panchromatic and hyperspectral images is proposed. First, the learning paradigm of multi-level supervision was introduced to fully fuse the complementary spatial and hyperspectral information of the panchromatic and hyperspectral images, whose training was integrated with the downstream target detection task. Second, the improved Mamba network was utilized to obtain the global information and grasp the target semantic features with higher precision. Finally, a new vision embedding method was designed to enhance the network’s perception of weak targets. The proposed method was validated on a hyperspectral-panchromatic fusion image target detection dataset, the results showed that it had a higher precision for weak target detection.