Spectral Correlation-Based Diverse Band Selection for Hyperspectral Image Classification

高光谱成像 计算机科学 光谱带 模式识别(心理学) 人工智能 水准点(测量) 稀疏逼近 选择(遗传算法) 维数之咒 相关性 缩小 数学 遥感 地理 几何学 程序设计语言 地质学 大地测量学
作者
Mingyang Ma,Shaohui Mei,Fan Li,Yaoyang Ge,Qian Du
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:61: 1-13 被引量:7
标识
DOI:10.1109/tgrs.2023.3263580
摘要

Band selection which can reduce the spectral dimensionality effectively, has become one of the most popular topics in hyperspectral image (HSI) analysis. Recently, sparse representation based band selection (BS) has emerged as a popular tool. The existing sparse models mainly focus on minimizing reconstruction error and sparsity, while do not fully exploit the unique correlations among hundreds of continuous bands, which may cause representative bands missed and highly-correlated bands selected. Therefore, this paper proposes the spectral correlation based diverse band selection (SCDBS) for HSIs to improve representativeness and diversity of the selected bands. Specifically, a correlation derived weight is used to perform weighted sparse reconstruction to select the bands that are more correlated to the whole HSI, and a correlation minimization term is designed to remove the highly-correlated bands simultaneously. In addition, the proposed method imposes an adjustable sparse constraint by using an ℓ 2,0<p≤1 norm, which extends and unifies the commonly used ℓ 2,1 norm to provide more flexible sparsity level. To optimize the proposed BS model, an iteration algorithm with relatively low computational cost is designed, of which the convergence is theoretically presented. Experimental results on three benchmark datasets have demonstrated that the proposed SCDBS outperforms state-of-the-art methods in HSI classification.
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