Multiple-Instance Metric Learning Network for Hyperspectral Target Detection.

Target detection becomes increasingly important in hyperspectral image (HSI) analysis but is limited by difficulties in acquiring accurate pixel-level training labels. This article proposes a multiple-instance metric learning neural network (MIML-Net) for hyperspectral target detection tasks, which only requires region-level labels and greatly alleviates the laborious pixel-level annotation problems. Our method learns the embeddings of regions with weak labels under attention-based multiple-instance learning (MIL) framework. Based on which, we impose a novel metric-based regularizer to constrain target and background embeddings to two learnable compact clusters with distinct centroids, which further boosts the spectral feature representation ability. The proposed metric-based regularizer enforces a discriminative detector due to its capability to reduce the intraclass variations and encourage the interclass separations simultaneously. Extensive experimental results from both simulated and real-field datasets demonstrate the effectiveness of the proposed MIML-Net in comparison with the state-of-the-art weakly supervised techniques.