Open AccessAn Optimization Method for Hyperspectral Endmember Extraction Based on K-SVD
Author(s) -
Xiaoxiao Feng,
Lei He,
Ya Zhang,
Yun Tang
Publication year - 2019
Publication title -
photogrammetric engineering and remote sensing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.483
H-Index - 127
eISSN - 2374-8079
pISSN - 0099-1112
DOI - 10.14358/pers.85.12.879
Subject(s) - endmember , hyperspectral imaging , singular value decomposition , pattern recognition (psychology) , mathematics , artificial intelligence , pixel , extraction (chemistry) , computer science , algorithm , chemistry , chromatography
Mixed pixels are common in hyperspectral imagery ( HSI ). Due to the complexity of the ground object distribution, some end-member extraction methods cannot obtain good results and the processes are complex. Therefore, this paper proposes an optimization method for HSI endmember extraction, which improves the accuracy of the results based on K-singular value decomposition ( K-SVD ). The proposed method comprises three core steps. (1) Based on the contribution value of initial endmembers, partially observed data selected according to the appropriate confidence participate in the calculation. (2) Construction of the error model to eliminate the background noise. (3) Using the K-SVD to perform column-by-column iteration on the endmembers to achieve the overall optimality. Experiments with three real images are applied, demonstrating the proposed method can improve the overall endmember accuracy by 15.1%–55.7% compared with the original methods.