Hyperspectral Target Detection via Multi-Instance Self-Attention Semantic Feature Extraction

Hyperspectral target detection tasks in remote sensing are frequently constrained by the challenge of obtaining pixel-level labels. Despite this challenge, acquiring region-level labels in hyperspectral images is more feasible. Consequently, researchers frequently turn to weakly supervised learning techniques, such as multi-instance learning, to address this issue. This paper proposes a multi-instance neural network with self-attention semantic modeling for hyperspectral target detection. Under semantic modeling, the network adaptively extracts representative spectral features from bag-level annotated hyperspectral data. The spectral features of the target and background are then clearly differentiated by metric learning and classification tasks. The proposed method demonstrates superior effectiveness in weakly labeled hy-perspectral target detection on both simulated and real-field datasets.