Multivariate image processing
著者
書誌事項
Multivariate image processing
（Digital signal and image processing series）
ISTE , J. Wiley, 2010
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
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 availability 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.
目次
1. Introduction to Multivariate Image Processing from the Basics to New Challenges. 2. Registration. 3. Fusion of SAR and Optical Data. 4. Fusion of Satellite Images at Different Resolutions. 5. Multitemporal Processing and Change Detection. 6. Bayesian Approach to Linear Spectral Mixture Analysis. 7. Detection and Tracking of Emission Rays in Radioastronomy. 8. Wavelet Transform for the Denoising of Multivariate Images. 9. Bayesian Approach for Polarization-encoded Image Analysis. 10. Unsupervised Classification for Multivariate Images. 11. Noise Estimation.
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