Recent advances in hyperspectral unmixing using sparse techniques and deep learning

Abstract

Spectral unmixing is an important technique for remotely sensed hyperspectral image interpretation that expresses each (possibly mixed) pixel vector as a combination of pure spectral signatures (endmembers) and their fractional abundances. Recently, sparse unmixing and deep learning have emerged as two powerful approaches for spectral unmixing. In this chapter, we focus on two particularly innovative contributions. First, we provide an overview of recent advances in semi-supervised sparse unmixing algorithms, with particular emphasis on techniques that include spatial–contextual information for a better scene interpretation. These algorithms require a spectral library of signatures available a priori to conduct the unmixing. Then, we describe new developments in the use of deep learning for spectral unmixing purposes, focusing on a new fully unsupervised deep auto-encoder network (DAEN) method. Our experiments with simulated and real hyperspectral datasets demonstrate the competitive advantages of these innovative approaches over some well-established unmixing methods, revealing that these methods are currently at the forefront of hyperspectral unmixing.