Multiple-Instance Metric Learning Network for Hyperspectral Target Detection
Bo Yang, Yi He, Changzhe Jiao, Xiao Pan, Guozhen Wang, Lei Wang, Jinjian Wu
Abstract
Target detection becomes increasingly important in hyperspectral image analysis but is limited by difficulties in acquiring accurate pixel-level training labels. This paper 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 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 intra-class variations and encourage the inter-class separations simultaneously. Extensive experimental results from both simulated and real-field data sets demonstrate the effectiveness of the proposed MIML-Net in comparison with the state-of-the-art weakly supervised techniques.