Hyperspectral unmixing: geometrical, statistical, and sparse regression-based approaches

José M. Bioucas-Dias; Antonio Plaza

doi:10.1117/12.870780

22 October 2010 Hyperspectral unmixing: geometrical, statistical, and sparse regression-based approaches

Proceedings Volume 7830, Image and Signal Processing for Remote Sensing XVI; 78300A (2010) https://doi.org/10.1117/12.870780
Event: SPIE Remote Sensing, 2010, Toulouse, France

Abstract

Hyperspectral instruments acquire electromagnetic energy scattered within their ground instantaneous field view in hundreds of spectral channels with high spectral resolution. Very often, however, owing to low spatial resolution of the scanner or to the presence of intimate mixtures (mixing of the materials at a very small scale) in the scene, the spectral vectors (collection of signals acquired at different spectral bands from a given pixel) acquired by the hyperspectral scanners are actually mixtures of the spectral signatures of the materials present in the scene. Given a set of mixed spectral vectors, spectral mixture analysis (or spectral unmixing) aims at estimating the number of reference materials, also called endmembers, their spectral signatures, and their fractional abundances. Spectral unmixing is, thus, a source separation problem where, under a linear mixing model, the sources are the fractional abundances and the endmember spectral signatures are the columns of the mixing matrix. As such, the independent component analysis (ICA) framework came naturally to mind to unmix spectral data. However, the ICA crux assumption of source statistical independence is not satisfied in spectral applications, since the sources are fractions and, thus, non-negative and sum to one. As a consequence, ICA-based algorithms have severe limitations in the area of spectral unmixing, and this has fostered new unmixing research directions taking into account geometric and statistical characteristics of hyperspectral sources. This paper presents an overview of the principal research directions in hyperspectral unmixing. The presentations is organized into four main topics: i) mixing models, ii) signal subspace identification, iii) geometrical-based spectral unmixing, (iv) statistical-based spectral unmixing, and (v) sparse regression-based unmixing. In each topic, we describe what physical or mathematical problems are involved and summarize state-of-the-art algorithms to address these problems.

Citation Download Citation

José M. Bioucas-Dias and Antonio Plaza "Hyperspectral unmixing: geometrical, statistical, and sparse regression-based approaches", Proc. SPIE 7830, Image and Signal Processing for Remote Sensing XVI, 78300A (22 October 2010); https://doi.org/10.1117/12.870780

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