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SUPERVISED NONLINEAR UNMIXING OF HYPERSPECTRAL IMAGES USING A PRE-IMAGE METHODS
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@MISC{Nguyen_supervisednonlinear,
author = {N. H. Nguyen and J. Chen and C. Richard and P. Honeine and C. Theys},
title = {SUPERVISED NONLINEAR UNMIXING OF HYPERSPECTRAL IMAGES USING A PRE-IMAGE METHODS},
year = {}
}
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Abstract
Abstract. Spectral unmixing is an important issue to analyze remotely sensed hyperspectral data. This involves the decomposition of each mixed pixel into its pure endmember spectra, and the estimation of the abundance value for each endmember. Although linear mixture models are often considered because of their simplicity, there are many situations in which they can be advantageously replaced by nonlinear mixture models. In this chapter, we derive a supervised kernel-based unmixing method that relies on a pre-image problem-solving technique. The kernel selection problem is also briefly considered. We show that partially-linear kernels can serve as an appropriate solution, and the nonlinear part of the kernel can be advantageously designed with manifold-learning-based techniques. Finally, we incorporate spatial information into our method in order to improve unmixing performance. 1
Keyphrases
nonlinear mixture model linear mixture model sensed hyperspectral data mixed pixel pure endmember spectrum kernel selection problem spatial information appropriate solution important issue pre-image problem-solving technique unmixing performance supervised kernel-based unmixing method nonlinear part partially-linear kernel manifold-learning-based technique many situation spectral unmixing abundance value
