详细信息
Non-Negative Matrix Factorization with Averaged Kurtosis and Manifold Constraints for Blind Hyperspectral Unmixing ( SCI-EXPANDED收录) 被引量:1
文献类型:期刊文献
英文题名:Non-Negative Matrix Factorization with Averaged Kurtosis and Manifold Constraints for Blind Hyperspectral Unmixing
作者:Song, Chunli Lu, Linzhang Zeng, Chengbin
第一作者:Song, Chunli;宋春丽
通信作者:Lu, LZ[1];Lu, LZ[2]
机构:[1]Guizhou Normal Univ, Sch Math Sci, Guiyang 550025, Peoples R China;[2]Guizhou Inst Technol, Sch Big Data, Lab Elect Power Big Data Guizhou Prov, Guiyang 550025, Peoples R China;[3]Xiamen Univ, Sch Math Sci, Xiamen 361005, Peoples R China;[4]Moutai Inst, Dept Automat, Renhuai 564507, Peoples R China
第一机构:Guizhou Normal Univ, Sch Math Sci, Guiyang 550025, Peoples R China
通信机构:corresponding author), Guizhou Normal Univ, Sch Math Sci, Guiyang 550025, Peoples R China;corresponding author), Xiamen Univ, Sch Math Sci, Xiamen 361005, Peoples R China.
年份:2024
卷号:16
期号:11
外文期刊名:SYMMETRY-BASEL
收录:;Scopus(收录号:2-s2.0-85210441459);WOS:【SCI-EXPANDED(收录号:WOS:001366527900001)】;
基金:This research was partially funded by the National Natural Science Foundation of China under Grant 12161020, 12061025, 61966006, 32260225, and partially funded by Guizhou Provincial Basis Research Program (Natural Science) QKHJC-ZK[2024]YB528, Guizhou Provincial Science and Technology Projects QIANKEHEJICHU-ZK [2021] Key 038.
语种:英文
外文关键词:hyperspectral images (HSIs); nonnegative matrix factorization (NMF); manifold constraints; average kurtosis constraints; hyperspectral unmixing (HU)
摘要:The Nonnegative Matrix Factorization (NMF) algorithm and its variants have gained widespread popularity across various domains, including neural networks, text clustering, image processing, and signal analysis. In the context of hyperspectral unmixing (HU), an important task involving the accurate extraction of endmembers from mixed spectra, researchers have been actively exploring different regularization techniques within the traditional NMF framework. These techniques aim to improve the precision and reliability of the endmember extraction process in HU. In this study, we propose a novel HU algorithm called KMBNMF, which introduces an average kurtosis regularization term based on endmember spectra to enhance endmember extraction, additionally, it integrates a manifold regularization term into the average kurtosis-constrained NMF by constructing a symmetric weight matrix. This combination of these two regularization techniques not only optimizes the extraction process of independent endmembers but also improves the part-based representation capability of hyperspectral data. Experimental results obtained from simulated and real-world hyperspectral datasets demonstrate the competitive performance of the proposed KMBNMF algorithm when compared to state-of-the-art algorithms.
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