Supervised nonlinear unmixing of hyperspectral images using a pre-image method - Laboratoire Hippolyte Fizeau
Article Dans Une Revue EAS Publications Series Année : 2013

Supervised nonlinear unmixing of hyperspectral images using a pre-image method

Résumé

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.
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Dates et versions

hal-01964950 , version 1 (15-10-2024)

Identifiants

Citer

Nguyen Hoang Nguyen, Jie Chen, Cédric Richard, Céline Theys, Paul Honeine. Supervised nonlinear unmixing of hyperspectral images using a pre-image method. EAS Publications Series, 2013, EAS Publications Series, 59, pp.417 - 437. ⟨10.1051/eas/1359019⟩. ⟨hal-01964950⟩
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