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Open AccessArticle

Superpixel Segmentation of Hyperspectral Images Based on Entropy and Mutual Information

Department of Test and Control Engineering, School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
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Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(4), 1261; https://doi.org/10.3390/app10041261 (registering DOI)
Received: 29 November 2019 / Revised: 7 February 2020 / Accepted: 10 February 2020 / Published: 13 February 2020
(This article belongs to the Special Issue Hyperspectral Imaging, Methods and Applications)
Superpixel segmentation (SS) methods have been proven to be feasible in improving the performance of hybrid algorithms on hyperspectral images (HSIs). In this paper, a superpixel segmentation algorithm based on the information measures with color histogram driving (IM-CHD) was proposed. First, Shannon entropy was applied to measure the image information and preliminarily select spectral bands. Mutual information (MI) is derived from the concept of entropy and measures the statistical dependence between two random variables. Also, MI can effectively identify the redundant spectral bands. Therefore, in this paper, both MI and color matching functions (CMF) were used to select the most useful spectral bands. Second, the selected spectral bands were combined into a false color image containing the main spectral information. A local optimization algorithm named “hill climbing” was used to achieve the superpixel segmentation. Finally, parameter selection experiments and comparative experiments were performed on two hyperspectral data sets. The experimental results showed that the IM-CHD method was more efficient and accurate than other state-of-the-art methods. View Full-Text
Keywords: hyperspectral images; superpixel segmentation; entropy; mutual information; color matching functions hyperspectral images; superpixel segmentation; entropy; mutual information; color matching functions
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Lin, L.; Zhang, S. Superpixel Segmentation of Hyperspectral Images Based on Entropy and Mutual Information. Appl. Sci. 2020, 10, 1261.

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