Sparse Unmixing of Hyperspectral Images Based on Adaptive Total  Variation and Low-Rank Constraints

XU Chen-guang; GUO Yu; LI Feng; LIU Yi; LI Yan; DENG Chen-zhi; LIU Yan-de

doi:10.3964/j.issn.1000-0593(2025)04-1071-11

Spectroscopy and Spectral Analysis, Volume. 45, Issue 4, 1071(2025)

Sparse Unmixing of Hyperspectral Images Based on Adaptive Total Variation and Low-Rank Constraints

XU Chen-guang^1,2, GUO Yu¹, LI Feng¹, LIU Yi¹, LI Yan¹, DENG Chen-zhi^1、*, and LIU Yan-de²

¹Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, Nanchang330099, China

²School of Intelligent Electromechanical Equipment Innovation Research Institute， East China Jiaotong University， Nanchang 330013， China

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XU Chen-guang, GUO Yu, LI Feng, LIU Yi, LI Yan, DENG Chen-zhi, LIU Yan-de. Sparse Unmixing of Hyperspectral Images Based on Adaptive Total Variation and Low-Rank Constraints[J]. Spectroscopy and Spectral Analysis, 2025, 45(4): 1071

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Paper Information

Received: Mar. 17, 2024

Accepted: Apr. 24, 2025

Published Online: Apr. 24, 2025

The Author Email: DENG Chen-zhi (372472617@qq.com)

DOI:10.3964/j.issn.1000-0593(2025)04-1071-11

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology