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28 September 2009 A fast sequential endmember extraction algorithm based on unconstrained linear spectral unmixing
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Abstract
Spectral unmixing is an important tool for interpreting remotely sensed hyperspectral scenes with sub-pixel precision. It relies on the identification of a set of spectrally pure components (called endmembers) and the estimation of the fractional abundance of each endmember in each pixel of the scene. Fractional abundance estimation is generally subject to two constraints: non-negativity of estimated fractions and sum-to-one for all abundance fractions of endmembers in each single pixel. Over the last decade, several algorithms have been proposed for simultaneous and sequential extraction of image endmembers from hyperspectral scenes. In this paper, we develop a new sequential algorithm that automatically extracts endmembers by using an unconstrained linear mixture model. Our assumption is that fractional abundance estimation using a set of properly selected image endmembers should naturally incorporate the constraints mentioned above, while imposing the constraints for an inadequate set of spectral endmembers may introduce errors in the model. Our proposed approach first applies an unconstrained linear mixture model and then uses a new metric for measuring the deviation of the unconstrained model with regards to the ideal, fully constrained model. This metric is used to derive a set of spectral endmembers which are then used to unmix the original scene. The proposed algorithm is experimentally compared to other algorithms using both synthetic and real hyperspectral scenes collected by NASA/JPL's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS).
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Javier Plaza, Antonio Plaza, and Gabriel Martín "A fast sequential endmember extraction algorithm based on unconstrained linear spectral unmixing", Proc. SPIE 7477, Image and Signal Processing for Remote Sensing XV, 74770L (28 September 2009); https://doi.org/10.1117/12.830734
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