A method of minimum volume simplex analysis constrained unmixing for hyperspectral image

Jinlin Zou; Jinhui Lan; Yiliang Zeng; Hongtao Wu

doi:10.1117/12.2281598

21 July 2017 A method of minimum volume simplex analysis constrained unmixing for hyperspectral image

Jinlin Zou, Jinhui Lan, Yiliang Zeng, Hongtao Wu

Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 104202Y (2017) https://doi.org/10.1117/12.2281598
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China

Abstract

The signal recorded by a low resolution hyperspectral remote sensor from a given pixel, letting alone the effects of the complex terrain, is a mixture of substances. To improve the accuracy of classification and sub-pixel object detection, hyperspectral unmixing(HU) is a frontier-line in remote sensing area. Unmixing algorithm based on geometric has become popular since the hyperspectral image possesses abundant spectral information and the mixed model is easy to understand. However, most of the algorithms are based on pure pixel assumption, and since the non-linear mixed model is complex, it is hard to obtain the optimal endmembers especially under a highly mixed spectral data. To provide a simple but accurate method, we propose a minimum volume simplex analysis constrained (MVSAC) unmixing algorithm. The proposed approach combines the algebraic constraints that are inherent to the convex minimum volume with abundance soft constraint. While considering abundance fraction, we can obtain the pure endmember set and abundance fraction correspondingly, and the final unmixing result is closer to reality and has better accuracy. We illustrate the performance of the proposed algorithm in unmixing simulated data and real hyperspectral data, and the result indicates that the proposed method can obtain the distinct signatures correctly without redundant endmember and yields much better performance than the pure pixel based algorithm.

Citation Download Citation

Jinlin Zou, Jinhui Lan, Yiliang Zeng, and Hongtao Wu "A method of minimum volume simplex analysis constrained unmixing for hyperspectral image", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104202Y (21 July 2017); https://doi.org/10.1117/12.2281598

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