Techniques and Applications of Hyperspectral Image Analysis gives an introduction to the field of image analysis using hyperspectral techniques, and includes definitions and instrument descriptions. Other imaging topics that are covered are segmentation, regression and classification. The book discusses how high quality images of large data files can be structured and archived. Imaging techniques also demand accurate calibration, and are covered in sections about multivariate calibration techniques. The book explains the most important instruments for hyperspectral imaging in more technical detail. A number of applications from medical and chemical imaging are presented and there is an emphasis on data analysis including modeling, data visualization, model testing and statistical interpretation.
Author(s): Hans Grahn, Paul Geladi
Edition: 1
Year: 2007
Language: English
Pages: 399
Tags: Информатика и вычислительная техника;Обработка медиа-данных;Обработка изображений;
Cover Page......Page 1
Title: Techniques and Applications of Hyperspectral Image Analysis......Page 3
ISBN 047001086X......Page 4
Contents (with page links)......Page 5
Preface......Page 13
REFERENCES......Page 15
List of Contributors......Page 16
List of Abbreviations......Page 18
1.2 DIGITAL IMAGES, MULTIVARIATE IMAGES AND HYPERSPECTRAL IMAGES......Page 22
1.3 HYPERSPECTRAL IMAGE GENERATION......Page 26
1.4 ESSENTIALS OF IMAGE ANALYSIS CONNECTING SCENE AND VARIABLE SPACES......Page 30
REFERENCES......Page 35
2.1 INTRODUCTION......Page 38
2.2 DATASET PRESENTATION......Page 39
2.3 TOOLS IN MIA......Page 42
2.4 MIA ANALYSIS CONCEPT: MASTER DATASET ILLUSTRATIONS......Page 49
2.5 CONCLUSIONS......Page 61
REFERENCES......Page 62
3.1 INTRODUCTION TO MULTISPECTRAL IMAGING IN AGRICULTURE......Page 64
3.2 UNSUPERVISED CLASSIFICATION OF MULTISPECTRAL IMAGES......Page 67
3.3 SUPERVISED CLASSIFICATION OF MULTISPECTRAL IMAGES......Page 75
3.4 VISUALIZATION AND COLORING OF SEGMENTED IMAGES AND GRAPHS: CLASS COLORING......Page 83
3.5 CONCLUSIONS......Page 85
REFERENCES......Page 86
4.1 INTRODUCTION......Page 90
4.2 MATERIALS AND METHODS......Page 91
4.3 THEORY......Page 94
4.4 RESULTS AND DISCUSSION......Page 96
4.5 CONCLUSIONS......Page 106
REFERENCES......Page 108
5.1 INTRODUCTION......Page 110
5.2 EXAMPLE DATASETS AND SIMULATIONS......Page 113
5.3 COMPONENT ANALYSIS......Page 116
5.4 ORTHOGONAL MATRIX FACTORIZATION......Page 117
5.5 MAXIMUM LIKELIHOOD BASED APPROACHES......Page 134
5.6 CONCLUSIONS......Page 145
REFERENCES......Page 146
6.1 INTRODUCTION......Page 148
6.3 MULTIVARIATE IMAGE REGRESSION......Page 149
6.4 DATA CONDITIONING......Page 153
6.5 PLS REGRESSION OPTIMIZATION......Page 159
6.6 REGRESSION EXAMPLES......Page 161
6.7 CONCLUSIONS......Page 171
REFERENCES......Page 173
7.1 INTRODUCTION......Page 176
7.2 VALIDATION ISSUES......Page 177
7.3 CASE STUDIES......Page 181
7.4 DISCUSSION AND CONCLUSIONS......Page 198
7.5 REFLECTIONS ON 2-WAY CROSS-VALIDATION......Page 199
REFERENCES......Page 201
8.1 INTRODUCTION......Page 202
8.2 CLS MODELS......Page 203
8.3 DETECTION, CLASSIFICATION, AND QUANTIFICATION......Page 213
ACKNOWLEDGEMENTS......Page 221
REFERENCES......Page 222
9.2 THE NEED FOR CALIBRATION IN GENERAL......Page 224
9.3 THE NEED FOR IMAGE CALIBRATION......Page 225
9.4 RESOLUTION IN HYPERSPECTRAL IMAGES......Page 226
9.5 SPECTROSCOPIC DEFINITIONS......Page 228
9.6 CALIBRATION STANDARDS......Page 230
9.7 CALIBRATION IN HYPERSPECTRAL IMAGES......Page 234
REFERENCES......Page 240
10.1 INTRODUCTION......Page 242
10.2 APPLICATIONS: SOLVENT DIFFUSION AND PHARMACEUTICAL STUDIES......Page 258
10.3 DRUG RELEASE......Page 270
10.4 CONCLUSIONS......Page 275
REFERENCES......Page 276
11.1 INTRODUCTION......Page 282
11.2 DECRA APPROACH......Page 285
11.3 DECRA ALGORITHM......Page 290
H RELAXATION......Page 291
11.6 IMAGING METHODS......Page 292
11.7 PHANTOM IMAGES......Page 294
11.8 BRAIN IMAGES......Page 299
11.9 REGRESSION ANALYSIS......Page 303
REFERENCES......Page 306
12.1 INTRODUCTION......Page 310
12.2 SAMPLE CHARACTERIZATION AND CHEMICAL SPECIES DISTRIBUTION......Page 312
12.3 DETECTING CONTAMINATION AND DEFECTS IN AGRO-FOOD PRODUCTS......Page 318
12.4 OTHER AGRONOMIC AND BIOLOGICAL APPLICATIONS......Page 325
12.5 CONCLUSION......Page 327
REFERENCES......Page 328
13.1 INTRODUCTION......Page 334
13.2 PET......Page 336
13.3 PCA......Page 340
13.4 APPLICATION OF PCA IN PET......Page 343
13.5 CONCLUSIONS......Page 351
REFERENCES......Page 353
14.1 INTRODUCTION......Page 356
14.2 DATA MEASUREMENT......Page 359
14.3 SELECTION OF SAMPLES AND ACQUISITION SCHEMES......Page 361
14.4 WHAT, HOW MUCH AND WHERE......Page 364
14.5 DATA ANALYSIS AND THE UNDERLYING STRUCTURE OF THE DATA......Page 365
14.6 IMAGING WITH STATISTICALLY MEANINGFUL SPATIAL DIMENSIONS......Page 369
14.7 CHEMICAL CONTRAST......Page 371
14.8 MEASURE, COUNT AND COMPARE......Page 376
14.9 CORRELATING DATA TO SAMPLE PERFORMANCE AND/OR BEHAVIOR: THE VALUE OF NIRCI DATA......Page 380
14.10 CONCLUSIONS......Page 381
REFERENCES......Page 382
Index (with page links)......Page 384
COLOR PLATES......Page 390
