Skip to Main content Skip to Navigation
Journal articles

Nonlinear spectral unmixing of hyperspectral images using Gaussian processes

Abstract : This paper presents an unsupervised algorithm for nonlinear unmixing of hyperspectral images. The proposed model assumes that the pixel reflectances result from a nonlinear function of the abundance vectors associated with the pure spectral components. We assume that the spectral signatures of the pure components and the nonlinear function are unknown. The first step of the proposed method estimates the abundance vectors for all the image pixels using a Bayesian approach an a Gaussian process latent variable model for the nonlinear function (relating the abundance vectors to the observations). The endmembers are subsequently estimated using Gaussian process regression. The performance of the unmixing strategy is first evaluated on synthetic data. The proposed method provides accurate abundance and endmember estimations when compared to other linear and nonlinear unmixing strategies. An interesting property is its robustness to the absence of pure pixels in the image. The analysis of a real hyperspectral image shows results that are in good agreement with state of the art unmixing strategies and with a recent classification method.
Document type :
Journal articles
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-00818786
Contributor : Open Archive Toulouse Archive Ouverte (oatao) <>
Submitted on : Monday, April 29, 2013 - 10:48:53 AM
Last modification on : Thursday, March 18, 2021 - 2:16:20 PM
Long-term archiving on: : Tuesday, July 30, 2013 - 4:40:10 AM

File

Dobigeon_8942.pdf
Files produced by the author(s)

Identifiers

Citation

Yoann Altmann, Nicolas Dobigeon, Steve Mclaughlin, Jean-Yves Tourneret. Nonlinear spectral unmixing of hyperspectral images using Gaussian processes. IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2013, vol. 61, pp. 2442-2453. ⟨10.1109/TSP.2013.2245127⟩. ⟨hal-00818786⟩

Share

Metrics

Record views

427

Files downloads

923