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Dynamic programming-based band selection method for hyperspectral unmixing
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AI
DOI：10.1080/01431161.2023.2176724.png)
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Abstract
En 中文
Band selection (BS) is a method for optimizing feature selection, which aims to of reduce the computational complexity of processing hyperspectral image (HSI). However, there are many BS methods applied to image classification, target detection, and anomaly detection. Furthermore, the existing BS methods ignore the spatial structure of HSI. To solve the above problems, we proposed a dynamic programming-based BS method for hyperspectral unmixing. In this paper, we use the convex geometric structure of HSI in band space to project it into the subspace to obtain depth spectral features, then construct a dynamic programming model to select representative bands. To verify the effectiveness of the proposed method, experiments are conducted on three widely used datasets, and compared with three popular BS methods. The experimental results show that the proposed method has satisfactory performance in different evaluation indexes (including signal to reconstruction error (SRE), root mean square error (RMSE)) and three quantitative evaluations (average information entropy (AIE), average correlation coefficient (ACC) and average relative entropy (ARE)).
Keywords:
hyperspectral image
unmixing
band selection
spectral features
dynamic programming model
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2.6
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1.2W
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2.7W
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