Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares

Y. Altmann, N. Dobigeon, J-Y. Tourneret, S. McLaughlin
2011 2011 IEEE International Geoscience and Remote Sensing Symposium  
This paper studies a linear radial basis function network (RBFN) for unmixing hyperspectral images. The proposed RBFN assumes that the observed pixel reflectances are nonlinear mixtures of known endmembers (extracted from a spectral library or estimated with an endmember extraction algorithm), with unknown proportions (usually referred to as abundances). We propose to estimate the model abundances using a linear combination of radial basis functions whose weights are estimated using training
more » ... d using training samples. The main contribution of this paper is to study an orthogonal least squares algorithm which allows the number of RBFN centers involved in the abundance estimation to be significantly reduced. The resulting abundance estimator is combined with a fully constrained estimation procedure ensuring positivity and sum-to-one constraints for the abundances. The performance of the nonlinear unmixing strategy is evaluated with simulations conducted on synthetic and real data. Index Terms-Radial basis functions, hyperspectral image, spectral unmixing
doi:10.1109/igarss.2011.6049401 dblp:conf/igarss/AltmannDTM11 fatcat:akgmkfrqrjau7izsbf5j2334we