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Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL and Python, Third Edition 3rd New edition [Kõva köide]

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(Juelich Research Center, Germany)
  • Formaat: Hardback, 576 pages, kõrgus x laius: 235x156 mm, kaal: 953 g, 20 page follows page 230; 143 Illustrations, black and white
  • Ilmumisaeg: 06-Jun-2014
  • Kirjastus: CRC Press Inc
  • ISBN-10: 1466570377
  • ISBN-13: 9781466570375
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Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL and Python, Third Edition 3rd New editionSuurem pilt
  • Formaat: Hardback, 576 pages, kõrgus x laius: 235x156 mm, kaal: 953 g, 20 page follows page 230; 143 Illustrations, black and white
  • Ilmumisaeg: 06-Jun-2014
  • Kirjastus: CRC Press Inc
  • ISBN-10: 1466570377
  • ISBN-13: 9781466570375
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781466570375.html
Märksõnad:
Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL and Python, Third Edition introduces techniques used in the processing of remote sensing digital imagery. It emphasizes the development and implementation of statistically motivated, data-driven techniques. The author achieves this by tightly interweaving theory, algorithms, and computer codes.



See Whats New in the Third Edition:















Inclusion of extensive code in Python, with a cloud computing example New material on synthetic aperture radar (SAR) data analysis New illustrations in all chapters Extended theoretical development









The material is self-contained and illustrated with many programming examples in IDL. The illustrations and applications in the text can be plugged in to the ENVI system in a completely transparent fashion and used immediately both for study and for processing of real imagery. The inclusion of Python-coded versions of the main image analysis algorithms discussed make it accessible to students and teachers without expensive ENVI/IDL licenses. Furthermore, Python platforms can take advantage of new cloud services that essentially provide unlimited computational power.









The book covers both multispectral and polarimetric radar image analysis techniques in a way that makes both the differences and parallels clear and emphasizes the importance of choosing appropriate statistical methods. Each chapter concludes with exercises, some of which are small programming projects, intended to illustrate or justify the foregoing development, making this self-contained text ideal for self-study or classroom use.

Arvustused

"Dr. Canty continues to update his excellent remote sensing book to use modern computing techniques; this time adding scripts in the open source Python complementing his previous IDL/ENVI examples. This is a great reference for those looking to put remote sensing theory into practice." Michael Galloy, Tech-X Corporation



" includes 1) open source (Python) code, making the book more useful to readers without commercial software licenses, and 2) material on polarimetric SAR imagery, an increasingly important field of remote sensing, while continuing to focus on statistically motivated, data driven analysis methods. With this third edition Mort Cantys book has become even more indispensable." Allan Aasbjerg Nielsen, Technical University of Denmark



" the addition of open source Python code along with IDL will certainly guarantee a larger readership. For students/practitioners in the field of remote sensing who like to program and who prefer in-depth explanations, highly recommended." Gunter Menz,

Preface to the First Edition xi
Preface to the Second Edition xiii
Preface to the Third Edition xv
List of Figures xvii
Program Listings xxiii
1 Images, Arrays, and Matrices 1(34)
1.1 Multispectral satellite images
4(4)
1.2 Synthetic aperture radar images
8(3)
1.3 Algebra of vectors and matrices
11(7)
1.3.1 Elementary properties
11(3)
1.3.2 Square matrices
14(2)
1.3.3 Singular matrices
16(1)
1.3.4 Symmetric, positive definite matrices
16(1)
1.3.5 Linear dependence and vector spaces
17(1)
1.4 Eigenvalues and eigenvectors
18(3)
1.5 Singular value decomposition
21(2)
1.6 Finding minima and maxima
23(9)
1.7 Exercises
32(3)
2 Image Statistics 35(50)
2.1 Random variables
35(12)
2.1.1 Discrete random variables
36(1)
2.1.2 Continuous random variables
37(3)
2.1.3 Random vectors
40(2)
2.1.4 The normal distribution
42(3)
2.1.5 The gamma distribution and its derivatives
45(2)
2.2 Parameter estimation
47(6)
2.2.1 Random samples
47(3)
2.2.2 Sample distributions and interval estimators
50(3)
2.3 Multivariate distributions
53(8)
2.3.1 Vector sample functions and the data matrix
54(1)
2.3.2 Provisional means
55(4)
2.3.3 Real and complex multivariate sample distributions
59(2)
2.4 Bayes' Theorem, likelihood and classification
61(3)
2.5 Hypothesis testing
64(5)
2.6 Ordinary linear regression
69(7)
2.6.1 One independent variable
69(2)
2.6.2 Coefficient of determination (R2)
71(1)
2.6.3 More than one independent variable
72(2)
2.6.4 Regularization, duality and the Gram matrix
74(2)
2.7 Entropy and information
76(4)
2.7.1 Kullback—Leibler divergence
78(1)
2.7.2 Mutual information
78(2)
2.8 Exercises
80(5)
3 Transformations 85(42)
3.1 The discrete Fourier transform
85(6)
3.2 The discrete wavelet transform
91(14)
3.2.1 Haar wavelets
91(4)
3.2.2 Image compression
95(3)
3.2.3 Multiresolution analysis
98(7)
3.3 Principal components
105(6)
3.3.1 Image compression and reconstruction
107(1)
3.3.2 Primal solution
108(2)
3.3.3 Dual solution
110(1)
3.4 Minimum noise fraction
111(6)
3.4.1 Additive noise
112(3)
3.4.2 Minimum noise fraction in ENVI
115(2)
3.5 Spatial correlation
117(5)
3.5.1 Maximum autocorrelation factor
117(2)
3.5.2 Noise estimation
119(3)
3.6 Exercises
122(5)
4 Filters, Kernels and Fields 127(32)
4.1 The Convolution Theorem
127(5)
4.2 Linear filters
132(3)
4.3 Wavelets and filter banks
135(11)
4.3.1 One-dimensional arrays
135(6)
4.3.2 Two-dimensional arrays
141(5)
4.4 Kernel methods
146(6)
4.4.1 Valid kernels
146(4)
4.4.2 Kernel PCA
150(2)
4.5 Gibbs—Markov random fields
152(4)
4.6 Exercises
156(3)
5 Image Enhancement and Correction 159(72)
5.1 Lookup tables and histogram functions
159(2)
5.2 High-pass spatial filtering and feature extraction
161(15)
5.2.1 Sobel filter
161(2)
5.2.2 Laplacian-of-Gaussian filter
163(3)
5.2.3 OpenCV functions
166(4)
5.2.4 Invariant moments
170(6)
5.3 Panchromatic sharpening
176(9)
5.3.1 HSV fusion
176(3)
5.3.2 Brovey fusion
179(1)
5.3.3 PCA fusion
179(1)
5.3.4 DWT fusion
180(1)
5.3.5 A troves fusion
181(3)
5.3.6 A quality index
184(1)
5.4 Radiometric correction of polarimetric SAR imagery
185(11)
5.4.1 Speckle statistics
185(3)
5.4.2 Multi-look data
188(3)
5.4.3 Speckle filtering
191(5)
5.5 Topographic correction
196(16)
5.5.1 Rotation, scaling and translation
196(1)
5.5.2 Imaging transformations
197(2)
5.5.3 Camera models and RFM approximations
199(2)
5.5.4 Stereo imaging and digital elevation models
201(6)
5.5.5 Slope and aspect
207(1)
5.5.6 Illumination correction
208(4)
5.6 Image—image registration
212(14)
5.6.1 Frequency domain registration
212(5)
5.6.2 Feature matching
217(5)
5.6.3 Re-sampling with ground control points
222(4)
5.7 Exercises
226(5)
6 Supervised Classification Part 1 231(54)
6.1 Maximizing the a posteriori probability
232(1)
6.2 Training data and separability
233(5)
6.3 Maximum likelihood classification
238(5)
6.3.1 ENVI's maximum likelihood classifier
239(2)
6.3.2 A modified classifier for ENVI and a Python script
241(2)
6.4 Gaussian kernel classification
243(6)
6.5 Neural networks
249(17)
6.5.1 The neural network classifier
252(5)
6.5.2 Cost functions
257(1)
6.5.3 Backpropagation
258(6)
6.5.4 Overfitting and generalization
264(2)
6.6 Support vector machines
266(14)
6.6.1 Linearly separable classes
267(5)
6.6.2 Overlapping classes
272(2)
6.6.3 Solution with sequential minimal optimization
274(1)
6.6.4 Multiclass SVMs
275(2)
6.6.5 Kernel substitution
277(1)
6.6.6 A modified SVM classifier
278(2)
6.7 Exercises
280(5)
7 Supervised Classification Part 2 285(38)
7.1 Postprocessing
285(3)
7.1.1 Majority filtering
286(1)
7.1.2 Probabilistic label relaxation
286(2)
7.2 Evaluation and comparison of classification accuracy
288(12)
7.2.1 Accuracy assessment
289(5)
7.2.2 Cross-validation on the cloud
294(2)
7.2.3 Model comparison
296(4)
7.3 Adaptive boosting
300(5)
7.4 Classification of polarimetric SAR imagery
305(2)
7.5 Hyperspectral image analysis
307(13)
7.5.1 Spectral mixture modeling
307(3)
7.5.2 Unconstrained linear unmixing
310(1)
7.5.3 Intrinsic end-members and pixel purity
311(2)
7.5.4 Anomaly detection: The RX algorithm
313(2)
7.5.5 Anomaly detection: The kernel RX algorithm
315(5)
7.6 Exercises
320(3)
8 Unsupervised Classification 323(46)
8.1 Simple cost functions
324(2)
8.2 Algorithms that minimize the simple cost functions
326(13)
8.2.1 K-means. clustering
327(2)
8.2.2 Kernel K-means clustering
329(3)
8.2.3 Extended K-means clustering
332(3)
8.2.4 Agglomerative hierarchical clustering
335(2)
8.2.5 Fuzzy K-means clustering
337(2)
8.3 Gaussian mixture clustering
339(7)
8.3.1 Expectation maximization
340(3)
8.3.2 Simulated annealing
343(1)
8.3.3 Partition density
343(1)
8.3.4 Implementation notes
344(2)
8.4 Including spatial information
346(5)
8.4.1 Multiresolution clustering
346(2)
8.4.2 Spatial clustering
348(3)
8.5 A benchmark
351(2)
8.6 The Kohonen self-organizing map
353(4)
8.7 Image segmentation
357(6)
8.7.1 Segmenting a classified image
358(2)
8.7.2 Object-based classification
360(1)
8.7.3 The mean shift
361(2)
8.8 Exercises
363(6)
9 Change Detection 369(46)
9.1 Algebraic methods
370(1)
9.2 Postclassification comparison
371(1)
9.3 Principal components analysis (PCA)
371(6)
9.3.1 Iterated PCA
374(2)
9.3.2 Kernel PCA
376(1)
9.4 Multivariate alteration detection (MAD)
377(14)
9.4.1 Canonical correlation analysis (CCA)
379(2)
9.4.2 Orthogonality properties
381(3)
9.4.3 Scale invariance
384(1)
9.4.4 Iteratively re-weighted MAD
384(3)
9.4.5 Correlation with the original observations
387(1)
9.4.6 Regularization
387(4)
9.4.7 Postprocessing
391(1)
9.5 Decision thresholds
391(4)
9.6 Unsupervised change classification
395(3)
9.7 Change detection with polarimetric SAR imagery
398(5)
9.7.1 Single polarimetry: The gamma distribution
398(2)
9.7.2 Quad polarimetry: The complex Wishart distribution
400(3)
9.8 Radiometric normalization of multispectral imagery
403(8)
9.8.1 Scatterplot matching
404(2)
9.8.2 IR-MAD normalization
406(5)
9.9 Exercises
411(4)
A Mathematical Tools 415(14)
A.1 Cholesky decomposition
415(2)
A.2 Vector and inner product spaces
417(1)
A.3 Complex numbers, vectors and matrices
418(2)
A.4 Least squares procedures
420(5)
A.4.1 Recursive linear regression
420(2)
A.4.2 Orthogonal linear regression
422(3)
A.5 Proof of Theorem 7.1
425(4)
B Efficient Neural Network Training Algorithms 429(24)
B.1 The Hessian matrix
429(5)
B.1.1 The R-operator
430(3)
B.1.2 Calculating the, Hessian
433(1)
B.2 Scaled conjugate gradient training
434(10)
B.2.1 Conjugate directions
434(3)
B.2.2 Minimizing a quadratic function
437(3)
B.2.3 The algorithm
440(4)
B.3 Kalman filter training
444(8)
B.3.1 Linearization
444(2)
B.3.2 The algorithm
446(6)
B.4 A neural network classifier with hybrid training
452(1)
C ENVI Extensions in IDL 453(18)
C.1 Installation
453(1)
C.2 Documentation
454(17)
C.2.1 ENVI extensions for
Chapter 4
455(1)
C.2.2 ENVI extensions for
Chapter 5
455(5)
C.2.3 ENVI extensions for
Chapter 6
460(1)
C.2.4 ENVI extensions for
Chapter 7
461(2)
C.2.5 ENVI extensions for
Chapter 8
463(3)
C.2.6 ENVI extensions for
Chapter 9
466(5)
D Python Scripts 471(10)
D.1 Installation
471(2)
D.1.1 Required packages
471(1)
D.1.2 Eclipse and Pydev
472(1)
D.2 Documentation
473(8)
D.2.1 Utilities
473(1)
D.2.2 Scripts for
Chapter 1
473(1)
D.2.3 Scripts for
Chapter 4
474(1)
D.2.4 Scripts for
Chapter 5
474(2)
D.2.5 Scripts for
Chapter 6
476(1)
D.2.6 Scripts for
Chapter 7
477(1)
D.2.7 Scripts for
Chapter 8
478(1)
D.2.8 Scripts for
Chapter 9
479(2)
Mathematical Notation 481(2)
References 483(12)
Index 495