Nonnegative matrix factorization with spatial prior and reference spectra application to remote hyperspectral image understanding

Abstract

This paper deals with nonnegative matrix factorization (NMF) dedicated to unmixing of hyperspectral images (HSI). We propose several improvements to better relate the output endmember spectra to the physical properties of the input data: firstly, we introduce a regularization term which enforces the closeness of the output endmembers to automatically selected reference spectra. Secondly, we account for these reference spectra and their locations in the initialization matrices. We exemplify our methods on self-acquired HSIs. The first scene is compound of leaves at the macroscopic level. In a controlled environment, we extract the spectra of three pigments. The second scene is acquired from an airplane: we distinguish between vegetation, water, and soil.