Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data

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Springer Science & Business Media, Aug 12, 2004 - Computers - 322 pages
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Over the last fifty years, a large number of spaceborne and airborne sensors have been employed to gather information regarding the earth's surface and environment. As sensor technology continues to advance, remote sensing data with improved temporal, spectral, and spatial resolution is becoming more readily available. This widespread availability of enormous amounts of data has necessitated the development of efficient data processing techniques for a wide variety of applications. In particular, great strides have been made in the development of digital image processing techniques for remote sensing data. The goal has been efficient handling of vast amounts of data, fusion of data from diverse sensors, classification for image interpretation, and development of user-friendly products that allow rich visualization. This book presents some new algorithms that have been developed for high dimensional datasets, such as multispectral and hyperspectral imagery. The contents of the book are based primarily on research carried out by some members and alumni of the Sensor Fusion Laboratory at Syracuse University.
 

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Contents

Hyperspectral Sensors and Applications
11
13 Hyperspectral Systems
14
132 Spaceborne sensors
23
14 Ground Spectroscopy
27
15 Software for Hyperspectral Processing
29
16 Applications
30
162 Vegetation
33
163 Soils and Geology
38
62 MRF and Gibbs Distribution
161
622 Cliques Potential and Gibbs Distributions
162
63 MRF Modeling in Remote Sensing Applications
165
64 Optimization Algorithms
167
641 Simulated Annealing
168
642 Metropolis Algorithm
173
643 Iterated Conditional Modes Algorithm
175
65 Summary
177

164 Environmental Hazards and Anthropogenic Activity
39
17 Summary
40
Overview of Image Processing
51
22 Image File Formats
52
23 Image Distortion and Rectification
53
232 Distortion and Rectification
54
24 Image Registration
56
25 Image Enhancement
57
252 Geometric Geometric Operations
63
26 Image Classification
66
261 Supervised Classification
67
262 Unsupervised Classification
69
263 Crisp Classification Algorithms
71
264 Fuzzy Classification Algorithms
74
265 Classification Accuracy Assessment
76
27 Image Change Detection
79
28 Image Fusion
80
29 Automatic Target Recognition
81
210 Summary
82
Mutual Information A Similarity Measure for Intensity Based Image Registration
89
32 Mutual Information Similarity Measure
90
33 Joint Histogram Estimation Methods
93
332 OneStep Joint Histogram Estimation
94
34 Interpolation Induced Artifacts
95
35 Generalized Partial Volume Estimation of Joint Histograms
99
36 Optimization Issues in the Maximization of Ml
103
37 Summary
107
Independent Component Analysis
109
43 ICA Algorithms
113
432 Information Minimization Solution for ICA
115
433 ICA Solution through NonGaussianity Maximization
121
44 Application of ICA to Hyperspectral Imagery
123
441 Feature Extraction Based Model
124
442 Linear Mixture Model Based Model
125
443 An ICA algorithm for Hyperspectral Image Processing
126
45 Summary
129
Support Vector Machines
133
52 Statistical Learning Theory
135
521 Empirical Risk Minimization
136
522 Structural Risk Minimization
137
53 Design of Support Vector Machines
138
531 Linearly Separable Case
139
532 Linearly NonSeparable Case
143
533 NonLinear Support Vector Machines
146
54 SVMs for Multiclass Classification
148
541 One Against the Rest Classification
149
and Decision Tree Structure
150
544 Multiclass Objective Function
152
56 Summary
154
Markov Random Field Models
159
MI Based Registration of MultiSensor and MultiTemporal Images
181
72 Registration Consistency
183
73 MultiSensor Registration
184
732 Registration of Images Having Similar Spatial Resolutions
188
74 MultiTemporal Registration
190
75 Summary
197
Feature Extraction from Hyperspectral Data Using ICA
199
82 PCA MS ICA for Feature Extraction
200
Feature Extraction Algorithm ICAFE
202
Feature Extraction Algorithm UICAFE
203
85 Experimental Results
210
86 Summary
215
Hyperspectral Classification Using ICA Based Mixture Model
217
92 Independent Component Analysis Mixture Model ICAMM Theory
219
921 ICAMM Classification Algorithm
220
93 Experimental Methodology
222
931 Feature Extraction Techniques
223
Feature Ranking
224
934 Unsupervised Classification
225
95 Summary
233
Support Vector Machines for Classification of Multi and Hyperspectral Data
237
102 Parameters Affecting SVM Based Classification
239
103 Remote Sensing Images
241
1032 Hyperspectral Image
242
104 SVM Based Classification Experiments
243
1042 Choice of Optimizer
245
1043 Effect of Kernel Functions
248
105 Summary
254
An MRF Model Based Approach for Subpixel Mapping from Hyperspectral Data
257
112 MRF Model for Subpixel Mapping
259
113 Optimum Subpixel Mapping Classifier
261
114 Experimental Results
265
Subpixel Mapping from Multispectral Data
266
Subpixel Mapping from Hyperspectral Data
271
115 Summary
276
Image Change Detection and Fusion Using MRF Models
279
1221 Image Change Detection ICD Algorithm
281
1222 Optimum Detector
284
123 Illustrative Examples of Image Change Detection
285
Synthetic Data
287
Multispectral Remote Sensing Data
290
124 Image Fusion using an MRF model
292
1241 Image Fusion Algorithm
294
125 Illustrative Examples of Image Fusion
299
Hyperspectral Image Fusion
303
126 Summary
306
Color Plates
309
Index
317
Copyright

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Page xiii - Department of Computer Science and Engineering The University of Texas at Arlington, Arlington, TX 76019, USA {hpshen, das, kumar, zwangj8cse.uta.edu Abstract.
Page 306 - Optimal data fusion in multiple sensor detection systems," IEEE Transactions on Aerospace and Electronic Systems, AES-22, pp.

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About the author (2004)

Pramod K. Varshney is a Professor of Electrical Engineering and Computer Science at Syracuse University, Syracuse, NY.