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9 August 2018 Iterative method to decompose hyperspectral mixed pixel using barycentric coordinate
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Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 1080631 (2018) https://doi.org/10.1117/12.2502910
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China
Abstract
Spectral mixture analysis (also called spectral unmixing) is one of the important and effective techniques to estimate abundance fractions of materials present in the hyperspectral imagery. Linear spectral mixture modeling is widely used in solving the spectral unmixing problems as its conciseness and clarity of physical meaning. This paper presents a novel algorithm to produce fully constrained (i.e. non-negative and sum to one constrained) abundance using the barycentric coordinates in the n-simplex. To impose non-negative constraint on the abundance, the proposed method use a serious of orthogonal projections to find the fully constrained solution, which takes into account the geometric structure of hyperspectral data set. The proposed algorithm is in line with the least squares criterion. The efficiency and effectiveness of the resulting unmixing algorithm is demonstrated using both synthetic and real hyperspectral images.
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Nan Xu, Tianbo Liu, and Yi Ren "Iterative method to decompose hyperspectral mixed pixel using barycentric coordinate", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 1080631 (9 August 2018); https://doi.org/10.1117/12.2502910
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