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Book Sections Year : 2009

Bayesian approach to linear spectral mixture analysis

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Abstract

Book description : Multivariate imagery is now a very common tool in numerous applications, ranging from satellite remote sensing and astrophysics to biomedical imagery, monitoring of the environment or industrial inspection. Multivariate must be understood in th emost general way: color and multispectral imaging, but also multimodal, multisource or multitemporal imagery. In all the cases, the multivariate image corresponds to a set of standard grey level images. The avalability of the additional diversity, be it spectral temporal and s.o., provides an invaluable source of information, enabling to consider a wide range of new applications. However,in order to address these applications, theoretical developments are required in terms of signal and image processing, or, more generally speaking, information processing. As a matter of fact, most of the standard algorithms designed for grey level images do not generalize easily to multidimensional spaces and some specific derivations are required. This book aims at presenting the most recent advances in signal and image processing for the analysis of multivariate data. It should be helpful for electrical engineers, PhD students and researcher working in the field of signal processing, but also for any engineer dealing with some specific application where multidimensional data are processed.
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Dates and versions

hal-00439435 , version 1 (07-12-2009)

Identifiers

  • HAL Id : hal-00439435 , version 1

Cite

Saïd Moussaoui, David Brie, Cédric Carteret. Bayesian approach to linear spectral mixture analysis. Christophe Collet, Jocelyn Chanussot, Kacem Chehdi. Multivariate image processing, John Wiley - ISTE, pp.143-168, 2009. ⟨hal-00439435⟩
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