Remote Sensing Digital Image Analysis: An Introduction / Edition 4

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Revised and enlarged to reflect new developments in the field, the fourth edition of this well-established text provides an introduction to quantitative evaluation of satellite- and aircraft-derived remotely retrieved data. Each chapter covers the pros and cons of digital remotely sensed data, without detailed mathematical treatment of computer based algorithms, but in a manner conductive to an understanding of their capabilities and limitations.

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Product Details

  • ISBN-13: 9783540251286
  • Publisher: Springer-Verlag New York, LLC
  • Publication date: 12/2/2005
  • Edition description: REV
  • Edition number: 4
  • Pages: 464
  • Product dimensions: 6.40 (w) x 9.40 (h) x 0.90 (d)

Meet the Author

John Richards has been active in remote sensing research for the past 30 years, mainly in image analysis and imaging radar and is the author of four books, all published by Springer. He is an electrical engineering graduate from the University of New South Wales, Australia, and a Life Fellow of the IEEE. John was foundation Director of the Centre for Remote Sensing at the University of New South Wales in the 1980s, and has had a career that has taken him through teaching and research in remote sensing and engineering. He has also served in senior academic administration including as Dean of the College of Engineering and Computer Science, and Vice-President of the Australian National University. John is an Emeritus Professor of both the Australian National University and the University of New South Wales.

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Table of Contents

From the Contents: Sources and Characteristics of Remote Sensing Image Data.- Error Correction and Registration of Image Data.- The Interpretation of Digital Image Data.- Radiometric Enhancement Techniques.- Geometric Enhancement Using Image Domain Techniques.- Multispectral Transformations of Image Data.- Fourier Transformation of Image Data.- Supervised Classification Techniques.- Clustering and Unsupervised Classification.- Feature Reduction.- Image Classification Methodologies.- Data Fusion.- Interpretation of Hyperspectral Image Data.- Appendices.

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