Multiple-Instance Metric Learning Network for Hyperspectral Target Detection

Bo Yang

doi:10.1109/TGRS.2023.3291439

Multiple-Instance Metric Learning Network for Hyperspectral Target Detection

2023-01-01

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DOI：10.1109/TGRS.2023.3291439

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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.

Keywords:

Discriminative feature embedding

hyperspetral

metric learning (ML)

multiple-instance learning (MIL)

target detection

Multiple-Instance Metric Learning Network for Hyperspectral Target Detection

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