Orthogonal projection-based fully constrained spectral unmixing

Meiping Song; Hsiao-Chi Li; Yao Li; Cheng Gao; Chein-I Chang

doi:10.1117/12.2177429

21 May 2015 Orthogonal projection-based fully constrained spectral unmixing

Meiping Song, Hsiao-Chi Li, Yao Li, Cheng Gao, Chein-I Chang

Proceedings Volume 9501, Satellite Data Compression, Communications, and Processing XI; 95010G (2015) https://doi.org/10.1117/12.2177429
Event: SPIE Sensing Technology + Applications, 2015, Baltimore, MD, United States

Abstract

OSP has been used widely in detection and abundance estimation for about twenty years. But it can’t apply nonnegative and sum-to-one constraints when being used as an abundance estimator. Fully constrained least square algorithm does this well, but its time cost increases greatly as the number of endmembers grows. There are some tries for unmixing spectral under fully constraints from different aspects recently. Here in this paper, a new fully constrained unmixing algorithm is prompted based on orthogonal projection process, where a nearest projected point is defined onto the simplex constructed by endmembers. It is much easier, and it is faster than FCLS with the mostly same unmixing results. It is also compared with other two constrained unmixing algorithms, which shows its effectiveness too.

Citation Download Citation

Meiping Song, Hsiao-Chi Li, Yao Li, Cheng Gao, and Chein-I Chang "Orthogonal projection-based fully constrained spectral unmixing", Proc. SPIE 9501, Satellite Data Compression, Communications, and Processing XI, 95010G (21 May 2015); https://doi.org/10.1117/12.2177429

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