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Hyperspectral imaging technology has wide applications, and endmember extraction is a crucial step in leveraging this technology. Endmember extraction aims to identify the spectral signatures of major materials (endmembers) in the observed scenes.
Hottopixx methods are theoretically effective for endmember extraction problems, but they are computationally challenging due to the large size of the underlying linear programming (LP) problems.
The authors propose an efficient algorithm called "row and column expansion" (RCE) to solve the Hottopixx LP problems effectively. RCE exploits the sparsity of the optimal solutions and uses a column generation framework.
The authors also propose an enhanced postprocessing method called "cluster centroid choice" to improve the endmember extraction performance of Hottopixx.
Experiments on synthetic and semi-real hyperspectral datasets show that the proposed EEHT (Efficient and Effective Hottopixx) implementation can significantly reduce the computational time and provide more accurate estimations of endmember signatures compared to existing methods.
The key advantages of EEHT are its computational efficiency and enhanced endmember extraction performance, making it a promising approach for practical applications of hyperspectral imaging technology.
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by Tomohiko Miz... at arxiv.org 04-23-2024
https://arxiv.org/pdf/2404.13098.pdfDeeper Inquiries