Abstract:
This paper presents an unsupervised Bayesian algorithm for hyper-spectral image unmixing accounting for endmember variability. This variability is obtained by assuming th...Show MoreMetadata
Abstract:
This paper presents an unsupervised Bayesian algorithm for hyper-spectral image unmixing accounting for endmember variability. This variability is obtained by assuming that each pixel is a linear combination of random endmembers weighted by their corresponding abundances. An additive noise is also considered in the proposed model generalizing the normal compositional model. The proposed model is unsupervised since it estimates the abundances and both the mean and the covariance matrix of each endmember. A classification map indicating the class of each pixel is also obtained based on the estimated abundances. Simulations conducted on a real dataset show the potential of the proposed model in terms of unmixing performance for the analysis of hyperspectral images.
Published in: 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)
Date of Conference: 02-05 June 2015
Date Added to IEEE Xplore: 23 October 2017
ISBN Information:
Electronic ISSN: 2158-6276