Research on the Selection Method of Visually Significant Band for Ground Object Classification
YANG Guang1, HU Hao-wen1, JIN Chun-bai1, REN Chun-ying2*, WANG Long-guang1, WANG Qi1, LIU Wen-jing1*
1. Aviation University of Air Force, Changchun 130022, China
2. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Abstract：The selection of remote sensing image bands is a prerequisite for the application of remote sensing data. It aids in the visualization and interpretation of remote sensing images, enhances image quality, and highlights the differences between different surface features. This provides a foundational basis for target recognition, image classification, and change detection. However, the large number of hyperspectral image bands, that is, the high spectral dimension, brings great problems and challenges to the band combination of bloom images. Therefore, it is necessary to reduce the dimension of hyperspectral data. In research on band combination, to preserve the spectral characteristics of the original band, the feature selection method is the most reasonable dimensionality reduction approach. In the original data set, select a specific band to form a band subset, and then carry out band selection research. In this paper, an Improved adjacent subspace partition (IASP) method is designed, and a band selection model based on visual saliency is constructed. Finally, the Histogram-based Contrast algorithm is selected to select the significant band, and a Contrast experiment is designed to verify the effectiveness of the method using the data of the OrbitaHyperSpectral satellite.
Key words：Hyperspectral image; Visual salience; Band selection; Band combination; Optimal index factor
杨 桄，胡昊文，金椿柏，任春颖，王龙光，王 琪，刘文婧. 面向地物分类的高光谱视觉显著性波段选择方法研究[J]. 光谱学与光谱分析, 2025, 45(10): 2950-2959.
YANG Guang, HU Hao-wen, JIN Chun-bai, REN Chun-ying, WANG Long-guang, WANG Qi, LIU Wen-jing. Research on the Selection Method of Visually Significant Band for Ground Object Classification. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(10): 2950-2959.
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