| Summary: | Band selection (BS) is an effective pre-processing way to reduce the redundancy of hyperspectral data. Specifically, the band prioritization (BP) criterion plays an essential role since it can judge the importance of bands from a particular perspective. However, most of the existing methods select bands according to a single criterion, leading to incomplete band evaluation and insufficient generalization against different data sets. To address this problem, this work proposes a multi-criteria-based band selection (MCBS) framework, which innovatively treats BS as a multi-criteria decision-making (MCDM) problem. First, a decision matrix is constructed based on several typical BPs, so as to evaluate the bands from different focuses. Then, MCBS defines the global positive and negative idea solutions and selects bands according to their relative closeness to these solutions. Since each BP has a different capability to discriminate the bands, two weight estimation approaches are developed to adaptively balance the contributions of various criteria. Finally, this work also provides an extended version of MCBS, which incorporates the subspace partition strategy to reduce the correlation of the selected bands. In this paper, the classification task is used to evaluate the performance of the selected band subsets. Extensive experiments on three public data sets verify that the proposed method outperforms other state-of-the-art methods.
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