Nonlinear spectral unmixing of hyperspectral images using Gaussian processes
This addresses hyperspectral image analysis for remote sensing applications, but it appears incremental as it builds on existing nonlinear unmixing methods with a specific Bayesian and Gaussian process approach.
The paper tackles unsupervised nonlinear unmixing of hyperspectral images by modeling pixel reflectances as a nonlinear function of unknown abundance vectors and spectral signatures, using Bayesian estimation and Gaussian process regression. The result is evaluated on synthetic and real data, though no concrete numbers are provided.
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 consists of the Bayesian estimation of the abundance vectors for all the image pixels and 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 evaluated with simulations conducted on synthetic and real data.