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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 trainingdoi:10.1109/igarss.2011.6049401 dblp:conf/igarss/AltmannDTM11 fatcat:akgmkfrqrjau7izsbf5j2334we