Abstract
The high dimensionality of hyperspectral images (HSIs) brings great difficulty for their later data processing. Band selection, as a commonly used dimension reduction technique, is the selection of optimal band combinations from the original bands, while attempting to remove the redundancy between bands and maintain a good classification ability. In this study, a novel hybrid filter-wrapper band selection method is proposed by a three-step strategy, i.e., band subset decomposition, band selection and band optimization. Based on the information gain (IG) and the spectral curve of the hyperspectral dataset, the band subset decomposition technique is improved, and a random selection strategy is suggested. The implementation of the first two steps addresses the problem of reducing inter-band redundancy. An optimization strategy based on a gray wolf optimizer (GWO) ensures that the selected band combination has a good classification ability. The classification performance of the selected band combination is verified on the Indian Pines, Pavia University and Salinas hyperspectral datasets with the aid of support vector machine (SVM) with a five-fold cross-validation. By comparing the proposed IG-GWO method with five state-of-the-art band selection approaches, the superiority of the proposed method for HSIs classification is experimentally demonstrated on three well-known hyperspectral datasets.
Highlights
Sensed hyperspectral data which is collected by hyperspectral sensors consist of hundreds of contiguous spectral bands with high resolutions [1,2]
There are still two issues that need to be addressed: (1) the representative bands can be quickly obtained in terms of the score of each band in the filter-based method, but the correlation between the selected bands may be high; (2) some bands show a significant and indispensable effect on hyperspectral images (HSIs) classification when combined with other bands
Extensive experimental results over the three hyperspectral datasets presented here clearly prove the effectiveness of the proposed method (IG-gray wolf optimizer (GWO)), offering a solution to the aforementioned limitations and excelling compared to the other state-of-the-art band selection methods
Summary
Sensed hyperspectral data which is collected by hyperspectral sensors consist of hundreds of contiguous spectral bands with high resolutions [1,2]. There are still two issues that need to be addressed: (1) the representative bands can be quickly obtained in terms of the score of each band in the filter-based method, but the correlation between the selected bands may be high; (2) some bands show a significant and indispensable effect on HSIs classification when combined with other bands. Their score may not be high, so they may be abandoned in error. Extensive experimental results over the three hyperspectral datasets presented here clearly prove the effectiveness of the proposed method (IG-GWO), offering a solution to the aforementioned limitations and excelling compared to the other state-of-the-art band selection methods
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