Stock #4570
Practical methods for the optimal analysis of multispectral and hyperspectral image data. The field of remote sensing is a cross-disciplinary one, involving professionals
ranging from signal processing engineers to earth science researchers to private and public sector practitioners, in nearly every region of the globe. The Signal
Theory Approach offers powerful methods for analyzing the complex data involved in this field. In contrast to previous broad surveys of the subject, Signal
Theory Methods in Multispectral Remote Sensing focuses on the practical knowledge data users of all types must have to optimally analyze multispectral
and hyper-spectral image data.
Both a textbook and self-teaching reference for professionals in the field, this book covers the fundamentals of the analysis of multispectral and hyperspectral image
data from the point of view of signal processing engineering. Avoiding topics common to general treatments of remote sensing but not germane to practical
applications, it offers concise discussions of:
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Pattern recognition methods as applied to the analysis of multivariate remotely sensed data
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The scene and sensor parts of a passive optical remote sensing system
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The statistical approach, including first-and second-order decision boundaries, error estimation, feature selection, and clustering
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Spectral feature design, the
incorporation of spatial variations, noise in remote sensing systems, and other methods necessary for hyperspectral analysis
As hyperspectral data becomes more widely available, the need for practical ways to analyze the very large volume of hyperspectral data on a personal computer makes this an
extremely timely and useful reference for all professionals and researchers involved in remote sensing.