Nonlinear unmixing of hyperspectral data using BDRF and maximum likelihood algorithm

M. T. Rahman; M. S. Alam

doi:10.1117/12.720231

7 May 2007 Nonlinear unmixing of hyperspectral data using BDRF and maximum likelihood algorithm

M. T. Rahman, M. S. Alam

Proceedings Volume 6566, Automatic Target Recognition XVII; 65660J (2007) https://doi.org/10.1117/12.720231
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

In this paper, we proposed a nonlinear unmixing matching algorithm using bidirectional reflectance function (BDRF) and maximum liklihood estimation (MLE). Spectral unmixing algorithms are used to determine the contribution of multiple substances in a single pixel of a hyperspectral image. For any kind of unmixing model basic approach is to describe how different substances are combined in a composite spectrum. When a linear reationship exists between the fractional abundance of the substances, linear unmixing algorithms can determine the endmembers present in that particular pixel. When the relationship is not linear rather each substance is randomly distributed in a homogeneous way the mixing is called nonlinear. Though there are plenty of unmixing algorithms based on linear mixing models (LMM) but very few algorithms have developed to to unmix nonlinear data. We proposed a nonlinear unmixing technique using BDRF and MLE and tested our algorithm using both synthetic and real hyperspectral data.

Citation Download Citation

M. T. Rahman and M. S. Alam "Nonlinear unmixing of hyperspectral data using BDRF and maximum likelihood algorithm", Proc. SPIE 6566, Automatic Target Recognition XVII, 65660J (7 May 2007); https://doi.org/10.1117/12.720231

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