Signal and Image Processing for Remote Sensing, Second Edition, 2nd Edition

Signal and Image Processing for Remote Sensing, Second Edition, 2nd Edition

by C.H. Chen
Released February 2012
Publisher(s): CRC Press
ISBN: 9781439855973

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Book description

Continuing in the footsteps of the pioneering first edition, Signal and Image Processing for Remote Sensing, Second Edition explores the most up-to-date signal and image processing methods for dealing with remote sensing problems. Although most data from satellites are in image form, signal processing can contribute significantly in extracting information from remotely sensed waveforms or time series data. This book combines both, providing a unique balance between the role of signal processing and image processing.

Featuring contributions from worldwide experts, this book continues to emphasize mathematical approaches. Not limited to satellite data, it also considers signals and images from hydroacoustic, seismic, microwave, and other sensors. Chapters cover important topics in signal and image processing and discuss techniques for dealing with remote sensing problems. Each chapter offers an introduction to the topic before delving into research results, making the book accessible to a broad audience.

This second edition reflects the considerable advances that have occurred in the field, with 23 of 27 chapters being new or entirely rewritten. Coverage includes new mathematical developments such as compressive sensing, empirical mode decomposition, and sparse representation, as well as new component analysis methods such as non-negative matrix and tensor factorization. The book also presents new experimental results on SAR and hyperspectral image processing.

The emphasis is on mathematical techniques that will far outlast the rapidly changing sensor, software, and hardware technologies. Written for industrial and academic researchers and graduate students alike, this book helps readers connect the "dots" in image and signal processing.

New in This Edition

The second edition includes four chapters from the first edition, plus 23 new or entirely rewritten chapters, and 190 new figures. New topics covered include:

  • Compressive sensing
  • The mixed pixel problem with hyperspectral images
  • Hyperspectral image (HSI) target detection and classification based on sparse representation
  • An ISAR technique for refocusing moving targets in SAR images
  • Empirical mode decomposition for signal processing
  • Feature extraction for classification of remote sensing signals and images
  • Active learning methods in classification of remote sensing images
  • Signal subspace identification of hyperspectral data
  • Wavelet-based multi/hyperspectral image restoration and fusion

The second edition is not intended to replace the first edition entirely and readers are encouraged to read both editions of the book for a more complete picture of signal and image processing in remote sensing. See Signal and Image Processing for Remote Sensing (CRC Press 2006).

Table of contents

  1. Front Cover (1/2)
  2. Front Cover (2/2)
  3. Contents
  4. Preface
  5. Editor
  6. Contributors (1/2)
  7. Contributors (2/2)
  8. Chapter 1: On the Normalized Hilbert Transform and Its Applications to Remote Sensing (1/4)
  9. Chapter 1: On the Normalized Hilbert Transform and Its Applications to Remote Sensing (2/4)
  10. Chapter 1: On the Normalized Hilbert Transform and Its Applications to Remote Sensing (3/4)
  11. Chapter 1: On the Normalized Hilbert Transform and Its Applications to Remote Sensing (4/4)
  12. Chapter 2: Nyquist Pulse-Based Empirical Mode Decomposition and Its Application to Remote Sensing Problems (1/4)
  13. Chapter 2: Nyquist Pulse-Based Empirical Mode Decomposition and Its Application to Remote Sensing Problems (2/4)
  14. Chapter 2: Nyquist Pulse-Based Empirical Mode Decomposition and Its Application to Remote Sensing Problems (3/4)
  15. Chapter 2: Nyquist Pulse-Based Empirical Mode Decomposition and Its Application to Remote Sensing Problems (4/4)
  16. Chapter 3: Hydroacoustic Signal Classification Using Support Vector Machines (1/4)
  17. Chapter 3: Hydroacoustic Signal Classification Using Support Vector Machines (2/4)
  18. Chapter 3: Hydroacoustic Signal Classification Using Support Vector Machines (3/4)
  19. Chapter 3: Hydroacoustic Signal Classification Using Support Vector Machines (4/4)
  20. Chapter 4: Huygens Construction and the Doppler Effect in Remote Detection (1/4)
  21. Chapter 4: Huygens Construction and the Doppler Effect in Remote Detection (2/4)
  22. Chapter 4: Huygens Construction and the Doppler Effect in Remote Detection (3/4)
  23. Chapter 4: Huygens Construction and the Doppler Effect in Remote Detection (4/4)
  24. Chapter 5: Compressed Remote Sensing (1/4)
  25. Chapter 5: Compressed Remote Sensing (2/4)
  26. Chapter 5: Compressed Remote Sensing (3/4)
  27. Chapter 5: Compressed Remote Sensing (4/4)
  28. Chapter 6: Context-Dependent Classification : An Approach for Achieving Robust Remote Sensing Performance in Changing Conditions (1/6)
  29. Chapter 6: Context-Dependent Classification : An Approach for Achieving Robust Remote Sensing Performance in Changing Conditions (2/6)
  30. Chapter 6: Context-Dependent Classification : An Approach for Achieving Robust Remote Sensing Performance in Changing Conditions (3/6)
  31. Chapter 6: Context-Dependent Classification : An Approach for Achieving Robust Remote Sensing Performance in Changing Conditions (4/6)
  32. Chapter 6: Context-Dependent Classification : An Approach for Achieving Robust Remote Sensing Performance in Changing Conditions (5/6)
  33. Chapter 6: Context-Dependent Classification : An Approach for Achieving Robust Remote Sensing Performance in Changing Conditions (6/6)
  34. Chapter 7: NMF and NTF for Sea Ice SAR Feature Extraction and Classification (1/3)
  35. Chapter 7: NMF and NTF for Sea Ice SAR Feature Extraction and Classification (2/3)
  36. Chapter 7: NMF and NTF for Sea Ice SAR Feature Extraction and Classification (3/3)
  37. Chapter 8: Relating Time Series of Meteorological and Remote Sensing Indices to Monitor Vegetation Moisture Dynamics (1/4)
  38. Chapter 8: Relating Time Series of Meteorological and Remote Sensing Indices to Monitor Vegetation Moisture Dynamics (2/4)
  39. Chapter 8: Relating Time Series of Meteorological and Remote Sensing Indices to Monitor Vegetation Moisture Dynamics (3/4)
  40. Chapter 8: Relating Time Series of Meteorological and Remote Sensing Indices to Monitor Vegetation Moisture Dynamics (4/4)
  41. Chapter 9: Use of a Prediction-Error Filter in Merging High- and Low-Resolution Images (1/3)
  42. Chapter 9: Use of a Prediction-Error Filter in Merging High- and Low-Resolution Images (2/3)
  43. Chapter 9: Use of a Prediction-Error Filter in Merging High- and Low-Resolution Images (3/3)
  44. Chapter 10: Hyperspectral Microwave Atmospheric Sounding Using Neural Networks (1/6)
  45. Chapter 10: Hyperspectral Microwave Atmospheric Sounding Using Neural Networks (2/6)
  46. Chapter 10: Hyperspectral Microwave Atmospheric Sounding Using Neural Networks (3/6)
  47. Chapter 10: Hyperspectral Microwave Atmospheric Sounding Using Neural Networks (4/6)
  48. Chapter 10: Hyperspectral Microwave Atmospheric Sounding Using Neural Networks (5/6)
  49. Chapter 10: Hyperspectral Microwave Atmospheric Sounding Using Neural Networks (6/6)
  50. Chapter 11: Satellite Passive Millimeter-Wave Retrieval of Global Precipitation (1/6)
  51. Chapter 11: Satellite Passive Millimeter-Wave Retrieval of Global Precipitation (2/6)
  52. Chapter 11: Satellite Passive Millimeter-Wave Retrieval of Global Precipitation (3/6)
  53. Chapter 11: Satellite Passive Millimeter-Wave Retrieval of Global Precipitation (4/6)
  54. Chapter 11: Satellite Passive Millimeter-Wave Retrieval of Global Precipitation (5/6)
  55. Chapter 11: Satellite Passive Millimeter-Wave Retrieval of Global Precipitation (6/6)
  56. Chapter 12: On SAR Image Processing : From Focusing to Target Recognition (1/4)
  57. Chapter 12: On SAR Image Processing : From Focusing to Target Recognition (2/4)
  58. Chapter 12: On SAR Image Processing : From Focusing to Target Recognition (3/4)
  59. Chapter 12: On SAR Image Processing : From Focusing to Target Recognition (4/4)
  60. Chapter 13: Polarimetric SAR Techniques for Remote Sensing of the Ocean Surface (1/7)
  61. Chapter 13: Polarimetric SAR Techniques for Remote Sensing of the Ocean Surface (2/7)
  62. Chapter 13: Polarimetric SAR Techniques for Remote Sensing of the Ocean Surface (3/7)
  63. Chapter 13: Polarimetric SAR Techniques for Remote Sensing of the Ocean Surface (4/7)
  64. Chapter 13: Polarimetric SAR Techniques for Remote Sensing of the Ocean Surface (5/7)
  65. Chapter 13: Polarimetric SAR Techniques for Remote Sensing of the Ocean Surface (6/7)
  66. Chapter 13: Polarimetric SAR Techniques for Remote Sensing of the Ocean Surface (7/7)
  67. Chapter 14: An ISAR Technique for Refocusing Moving Targets in SAR Images (1/6)
  68. Chapter 14: An ISAR Technique for Refocusing Moving Targets in SAR Images (2/6)
  69. Chapter 14: An ISAR Technique for Refocusing Moving Targets in SAR Images (3/6)
  70. Chapter 14: An ISAR Technique for Refocusing Moving Targets in SAR Images (4/6)
  71. Chapter 14: An ISAR Technique for Refocusing Moving Targets in SAR Images (5/6)
  72. Chapter 14: An ISAR Technique for Refocusing Moving Targets in SAR Images (6/6)
  73. Chapter 15: Active Learning Methods in Classification of Remote Sensing Images (1/5)
  74. Chapter 15: Active Learning Methods in Classification of Remote Sensing Images (2/5)
  75. Chapter 15: Active Learning Methods in Classification of Remote Sensing Images (3/5)
  76. Chapter 15: Active Learning Methods in Classification of Remote Sensing Images (4/5)
  77. Chapter 15: Active Learning Methods in Classification of Remote Sensing Images (5/5)
  78. Chapter 16: Crater Detection Based on Marked Point Processes (1/3)
  79. Chapter 16: Crater Detection Based on Marked Point Processes (2/3)
  80. Chapter 16: Crater Detection Based on Marked Point Processes (3/3)
  81. Chapter 17: Probability Density Function Estimation for Classification of High-Resolution SAR Images (1/6)
  82. Chapter 17: Probability Density Function Estimation for Classification of High-Resolution SAR Images (2/6)
  83. Chapter 17: Probability Density Function Estimation for Classification of High-Resolution SAR Images (3/6)
  84. Chapter 17: Probability Density Function Estimation for Classification of High-Resolution SAR Images (4/6)
  85. Chapter 17: Probability Density Function Estimation for Classification of High-Resolution SAR Images (5/6)
  86. Chapter 17: Probability Density Function Estimation for Classification of High-Resolution SAR Images (6/6)
  87. Chapter 18: Random Forest Classification of Remote Sensing Data (1/2)
  88. Chapter 18: Random Forest Classification of Remote Sensing Data (2/2)
  89. Chapter 19: Sparse Representation for Target Detection and Classification in Hyperspectral Imagery (1/6)
  90. Chapter 19: Sparse Representation for Target Detection and Classification in Hyperspectral Imagery (2/6)
  91. Chapter 19: Sparse Representation for Target Detection and Classification in Hyperspectral Imagery (3/6)
  92. Chapter 19: Sparse Representation for Target Detection and Classification in Hyperspectral Imagery (4/6)
  93. Chapter 19: Sparse Representation for Target Detection and Classification in Hyperspectral Imagery (5/6)
  94. Chapter 19: Sparse Representation for Target Detection and Classification in Hyperspectral Imagery (6/6)
  95. Chapter 20: Integration of Full and Mixed Pixel Techniques to Obtain Thematic Maps with a Refined Resolution (1/4)
  96. Chapter 20: Integration of Full and Mixed Pixel Techniques to Obtain Thematic Maps with a Refined Resolution (2/4)
  97. Chapter 20: Integration of Full and Mixed Pixel Techniques to Obtain Thematic Maps with a Refined Resolution (3/4)
  98. Chapter 20: Integration of Full and Mixed Pixel Techniques to Obtain Thematic Maps with a Refined Resolution (4/4)
  99. Chapter 21: Signal Subspace Identification in Hyperspectral Imagery (1/4)
  100. Chapter 21: Signal Subspace Identification in Hyperspectral Imagery (2/4)
  101. Chapter 21: Signal Subspace Identification in Hyperspectral Imagery (3/4)
  102. Chapter 21: Signal Subspace Identification in Hyperspectral Imagery (4/4)
  103. Chapter 22: Image Classification and Object Detection Using Spatial Contextual Constraints (1/5)
  104. Chapter 22: Image Classification and Object Detection Using Spatial Contextual Constraints (2/5)
  105. Chapter 22: Image Classification and Object Detection Using Spatial Contextual Constraints (3/5)
  106. Chapter 22: Image Classification and Object Detection Using Spatial Contextual Constraints (4/5)
  107. Chapter 22: Image Classification and Object Detection Using Spatial Contextual Constraints (5/5)
  108. Chapter 23: Data Fusion for Remote-Sensing Applications (1/5)
  109. Chapter 23: Data Fusion for Remote-Sensing Applications (2/5)
  110. Chapter 23: Data Fusion for Remote-Sensing Applications (3/5)
  111. Chapter 23: Data Fusion for Remote-Sensing Applications (4/5)
  112. Chapter 23: Data Fusion for Remote-Sensing Applications (5/5)
  113. Chapter 24: Image Fusion in Remote Sensing with the Steered Hermite Transform (1/4)
  114. Chapter 24: Image Fusion in Remote Sensing with the Steered Hermite Transform (2/4)
  115. Chapter 24: Image Fusion in Remote Sensing with the Steered Hermite Transform (3/4)
  116. Chapter 24: Image Fusion in Remote Sensing with the Steered Hermite Transform (4/4)
  117. Chapter 25: Wavelet-Based Multi/Hyperspectral Image Restoration and Fusion (1/4)
  118. Chapter 25: Wavelet-Based Multi/Hyperspectral Image Restoration and Fusion (2/4)
  119. Chapter 25: Wavelet-Based Multi/Hyperspectral Image Restoration and Fusion (3/4)
  120. Chapter 25: Wavelet-Based Multi/Hyperspectral Image Restoration and Fusion (4/4)
  121. Chapter 26: Land Cover Estimation with Satellite Image Using Neural Network (1/2)
  122. Chapter 26: Land Cover Estimation with Satellite Image Using Neural Network (2/2)
  123. Chapter 27: Twenty-Five Years of Pansharpening : A Critical Review and New Developments (1/10)
  124. Chapter 27: Twenty-Five Years of Pansharpening : A Critical Review and New Developments (2/10)
  125. Chapter 27: Twenty-Five Years of Pansharpening : A Critical Review and New Developments (3/10)
  126. Chapter 27: Twenty-Five Years of Pansharpening : A Critical Review and New Developments (4/10)
  127. Chapter 27: Twenty-Five Years of Pansharpening : A Critical Review and New Developments (5/10)
  128. Chapter 27: Twenty-Five Years of Pansharpening : A Critical Review and New Developments (6/10)
  129. Chapter 27: Twenty-Five Years of Pansharpening : A Critical Review and New Developments (7/10)
  130. Chapter 27: Twenty-Five Years of Pansharpening : A Critical Review and New Developments (8/10)
  131. Chapter 27: Twenty-Five Years of Pansharpening : A Critical Review and New Developments (9/10)
  132. Chapter 27: Twenty-Five Years of Pansharpening : A Critical Review and New Developments (10/10)
  133. Back Cover

Product information

  • Title: Signal and Image Processing for Remote Sensing, Second Edition, 2nd Edition
  • Author(s): C.H. Chen
  • Release date: February 2012
  • Publisher(s): CRC Press
  • ISBN: 9781439855973