An endmember extraction method based on PCA and a new SGA algorithm

YongTao Ya; YuanXi Peng; Tian Jiang; JinHan Na

doi:10.1117/12.2579775

5 November 2020 An endmember extraction method based on PCA and a new SGA algorithm

YongTao Ya, YuanXi Peng, Tian Jiang, JinHan Na

Proceedings Volume 11567, AOPC 2020: Optical Sensing and Imaging Technology; 1156724 (2020) https://doi.org/10.1117/12.2579775
Event: Applied Optics and Photonics China (AOPC 2020), 2020, Beijing, China

Abstract

The spatial resolution of hyperspectral data is low, so there are a large number of mixed pixels, which is also one of the main reasons that reduce the accuracy of hyperspectral image target classification. Hyperspectral unmixing is an important subject in the field of remote sensing. Hyperspectral unmixing generally consists of three steps: reduction, endmember extraction and inversion. As one of the key steps of hyperspectral unmixing, efficient and rapid endmember extraction is an important object in hyperspectral remote sensing. In this paper, the endmember extraction of hyperspectral data is implemented based on PCA and a new SGA algorithm, which solves the dimension limitation of traditional SGA algorithm and the new SGA algorithm without data redundancy caused by data dimensionality reduction.

Citation Download Citation

YongTao Ya, YuanXi Peng, Tian Jiang, and JinHan Na "An endmember extraction method based on PCA and a new SGA algorithm", Proc. SPIE 11567, AOPC 2020: Optical Sensing and Imaging Technology, 1156724 (5 November 2020); https://doi.org/10.1117/12.2579775

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