Signal Theory Methods in Multispectral Remote Sensing

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9780471420286: Signal Theory Methods in Multispectral Remote Sensing
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An outgrowth of the author's extensive experience teaching seniorand graduate level students, this is both a thorough introductionand a solid professional reference.
* Material covered has been developed based on a 35-year researchprogram associated with such systems as the Landsat satelliteprogram and later satellite and aircraft programs.
* Covers existing aircraft and satellite programs and severalfuture programs

*An Instructor's Manual presenting detailed solutions to all theproblems in the book is available from the Wiley editorialdepartment.

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Practical methods for the optimal analysis of multispectral andhyperspectral image data

The field of remote sensing is a cross-disciplinary one,involving professionals ranging from signal processing engineers toearth science researchers to private and public sectorpractitioners, in nearly every region of the globe. The SignalTheory Approach offers powerful methods for analyzing the complexdata involved in this field–methods which may not be familiarto many in non-engineering fields. In contrast to previous broadsurveys of the subject, Signal Theory Methods in MultispectralRemote Sensing focuses on the practical knowledge data users of alltypes must have to optimally analyze multispectral andhyperspectral image data.

Both a textbook and self-teaching reference for professionals inthe field, this book covers the fundamentals of the analysis ofmultispectral and hyperspectral image data from the point of viewof signal processing engineering. Avoiding topics common to generaltreatments of remote sensing but not germane to practicalapplications, it offers concise discussions of:

  • Pattern recognition methods as applied to the analysis ofmultivariate remotely sensed data
  • The scene and sensor parts of a passive optical remote sensingsystem
  • The statistical approach, including first-and second-orderdecision boundaries, error estimation, feature selection, andclustering
  • Spectral feature design, the incorporation of spatialvariations, noise in remote sensing systems, and other methodsnecessary for hyperspectral analysis

As hyperspectral data becomes more widely available, the needfor practical ways to analyze the very large volume ofhyperspectral data on a personal computer makes this an extremelytimely and useful reference for all professionals and researchersinvolved in remote sensing.

About the Author:

DAVID A. LANDGREBE, PhD, is Professor Emeritus of Electrical Computer Engineering in the School of Electrical and Computer Engineering at Purdue University. Dr. Landgrebe is a former president of the IEEE Geoscience and Remote Sensing Society and recipient of the Society’s Distinguished Achievement Award. He is the coauthor of Remote Sensing: The Quantitative Approach and a contributor to numerous other publications.

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