Optics and Precision Engineering, Volume. 31, Issue 9, 1404(2023)

Spectral weighted sparse unmixing of hyperspectral images based on framelet transform

Chenguang XU... Hongyu XU, Chunyan YU and Chengzhi DENG* |Show fewer author(s)
Author Affiliations
  • Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing,Nanchang Institute of Technology, Nanchang330099, China
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    Hyperspectral sparse unmixing methods have attracted considerable attention, and most current sparse unmixing methods are implemented in the spatial domain; however, the hyperspectral data used by these methods complicate feature extraction owing to scattered information, redundancy, and noisy spatial signals. To improve the robustness and sparsity of the unmixing results of hyperspectral images, a spectral-weighted sparse unmixing method of hyperspectral images based on the framelet transform (SFSU) is proposed. First, we introduce the theoretical knowledge of hyperspectral sparse unmixing and the framelet transform. Following this, we develop a hyperspectral image unmixing model based on the framelet transform using this theory. In this model, a spectral-weighted sparse regularization term is added to construct the SFSU. Finally, to solve the SFSU model, an alternating direction method of multipliers is presented. According to the experimental results, the signal-to-reconstruction error ratio is found to increase by 12.4%-1 045%, and the probability of success (Ps) remains within 16% error. The proposed model demonstrates better anti-noise and sparse performance compared with other related sparse unmixing methods and yields better unmixing results.

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    Chenguang XU, Hongyu XU, Chunyan YU, Chengzhi DENG. Spectral weighted sparse unmixing of hyperspectral images based on framelet transform[J]. Optics and Precision Engineering, 2023, 31(9): 1404

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    Paper Information

    Category: Information Sciences

    Received: May. 18, 2022

    Accepted: --

    Published Online: Jun. 6, 2023

    The Author Email: DENG Chengzhi (dengcz@nit.edu.cn)

    DOI:10.37188/OPE.20233109.1404

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