Image analysis, classification and change detection in remote sensing : with algorithms for python / Morton John Canty.

Format:

Book

Author/Creator:

Canty, Morton John, author.

Language:

English

Subjects (All):

Remote sensing--Mathematics.

Remote sensing.

Image analysis--Mathematics.

Image analysis.

Image analysis--Data processing.

Python (Computer program language).

Physical Description:

1 online resource (536 pages)

Edition:

Fifth edition.

Place of Publication:

Boca Raton, FL : CRC Press, [2025]

Summary:

The fifth edition of this core textbook in advanced remote sensing maintains the same theoretical material with necessary updates. The software tools have evolved substantially, and the fifth edition replaces Python 2 with Python 3 and uses the high-level packages based on it, such as Colab, Pytorch, KERAS, Scikit-Learn.

Contents:

Cover

Half Title

Title Page

Copyright Page

Contents

Preface to the First Edition

Preface to the Second Edition

Preface to the Third Edition

Preface to the Fourth Edition

Preface to the Fifth Edition

Author Biography

1. Images, Arrays, and Matrices

1.1. Multispectral satellite images

1.2. Synthetic aperture radar images

1.3. Linear algebra of vectors and matrices

1.3.1. Elementary properties

1.3.2. Square matrices

1.3.3. Singular matrices

1.3.4. Symmetric, positive definite matrices

1.3.5. Linear dependence and vector spaces

1.3.6. Eigenvalues and eigenvectors

1.3.7. Singular value decomposition

1.3.8. Tensors

1.4. Finding minima and maxima

1.5. Exercises

2. Image Statistics

2.1. Random variables

2.1.1. Discrete random variables

2.1.2. Continuous random variables

2.1.3. Random vectors

2.1.4. The normal distribution

2.1.5. The gamma distribution and its derivatives

2.2. Parameter estimation

2.2.1. Random samples

2.2.2. Sample distributions and interval estimators

2.3. Multivariate distributions

2.3.1. Vector sample functions and the data matrix

2.3.2. Provisional means

2.3.3. Real and complex multivariate sample distributions

2.4. Bayes' Theorem, likelihood, and classification

2.5. Hypothesis testing

2.6. Ordinary linear regression

2.6.1. One independent variable

2.6.2. Coefficient of determination (R2)

2.6.3. More than one independent variable

2.6.4. Regularization, duality, and the Gram matrix

2.7. Entropy and information

2.7.1. Kullback-Leibler divergence

2.7.2. Mutual information

2.8. Exercises

3. Transformations

3.1. The discrete Fourier transform

3.2. The discrete wavelet transform

3.2.1. Haar wavelets

3.2.2. Image compression

3.2.3. Multiresolution analysis.

3.3. Principal components

3.3.1. Principal components on the GEE

3.3.2. Image compression and reconstruction

3.3.3. Primal solution

3.3.4. Dual solution

3.4. Minimum noise fraction

3.4.1. Additive noise

3.4.2. Minimum noise fraction via PCA

3.5. Spatial correlation

3.5.1. Maximum autocorrelation factor

3.5.2. Noise estimation

3.6. Exercises

4. Filters, Kernels, and Fields

4.1. The convolution theorem

4.2. Linear filters

4.3. Wavelets and filter banks

4.3.1. One-dimensional arrays

4.3.2. Two-dimensional arrays

4.4. Kernel methods

4.4.1. Valid kernels

4.4.2. Kernel PCA

4.5. Gibbs-Markov random fields

4.6. Exercises

5. Image Enhancement and Correction

5.1. Lookup tables and histogram functions

5.2. High-pass spatial filtering and feature extraction

5.2.1. Sobel filter

5.2.2. Laplacian-of-Gaussian filter

5.2.3. OpenCV and GEE algorithms

5.2.4. Invariant moments

5.3. Panchromatic sharpening

5.3.1. HSV fusion

5.3.2. Brovey fusion

5.3.3. PCA fusion

5.3.4. DWT fusion

5.3.5. A trous fusion

5.3.6. A quality index

5.4. Radiometric correction of polarimetric SAR imagery

5.4.1. Speckle statistics

5.4.2. Multilook data

5.4.3. Speckle filtering

5.5. Topographic correction

5.5.1. Rotation, scaling, and translation

5.5.2. Imaging transformations

5.5.3. Camera models and RFM approximations

5.5.4. Stereo imaging and digital elevation models

5.5.5. Slope and aspect

5.5.6. Illumination correction

5.6. Image-image registration

5.6.1. Frequency domain registration

5.6.2. Feature matching

5.6.3. Re-sampling with ground control points

5.7. Exercises

6. Supervised Classification Part 1

6.1. Maximizing the a posteriori probability

6.2. Training data and separability

6.3. Bayes maximum-likelihood classification.

6.3.1. Naive Bayes on the GEE

6.3.2. Scripts for supervised classification

6.4. Gaussian kernel classification

6.5. Neural networks

6.5.1. The neural network classifier

6.5.2. Cost functions

6.5.3. Back propagation

6.5.4. Deep learning networks

6.5.5. Over fitting and generalization

6.6. Support vector machines

6.6.1. Linearly separable classes

6.6.2. Overlapping classes

6.6.3. Solution with sequential minimal optimization

6.6.4. Multiclass SVMs

6.6.5. Kernel substitution

6.7. Exercises

7. Supervised Classification Part 2

7.1. Postprocessing

7.1.1. Majority filtering

7.1.2. Probabilistic label relaxation

7.2. Evaluation and comparison of classification accuracy

7.2.1. Accuracy assessment

7.2.2. Accuracy assessment on the GEE

7.2.3. Cross-validation on parallel architectures

7.2.4. Model comparison

7.3. Ensembles

7.3.1. Adaptive boosting

7.3.2. Binary decision trees and random forests

7.4. Classification of polarimetric SAR imagery

7.5. Hyperspectral image analysis

7.5.1. Spectral mixture modeling

7.5.2. Unconstrained linear unmixing

7.5.3. Intrinsic end-members and pixel purity

7.5.4. Anomaly detection: The RX algorithm

7.5.5. Anomaly detection: The kernel RX algorithm

7.6. Convolutional neural networks

7.6.1. Transfer learning

7.6.2. Semantic segmentation

7.7. Exercises

8. Unsupervised Classification

8.1. Simple cost functions

8.2. Algorithms that minimize the simple cost functions

8.2.1. K-means clustering

8.2.2. Kernel K-means clustering

8.2.3. Extended K-means clustering

8.2.4. Agglomerative hierarchical clustering

8.2.5. Fuzzy K-means clustering

8.3. Gaussian mixture clustering

8.3.1. Expectation maximization

8.3.2. Simulated annealing

8.3.3. Partition density

8.3.4. Implementation notes.

8.4. Including spatial information

8.4.1. Multiresolution clustering

8.4.2. Spatial clustering

8.5. A benchmark

8.6. The Kohonen self-organizing map

8.7. Image segmentation and the mean shift

8.8. Exercises

9. Change Detection

9.1. Naive methods

9.2. Principal components analysis (PCA)

9.2.1. Iterated PCA

9.2.2. Kernel PCA

9.3. Multivariate alteration detection

9.3.1. Canonical correlation analysis (CCA)

9.3.2. Orthogonality properties

9.3.3. Iteratively re-weighted MAD

9.3.4. Scale invariance

9.3.5. Regularization

9.3.6. Postprocessing

9.3.7. Unsupervised change classification

9.3.8. iMAD on the Google Earth Engine

9.4. Change detection with polarimetric SAR imagery

9.4.1. Scalar imagery: the gamma distribution

9.4.2. Polarimetric imagery: the complex Wishart distribution

9.4.3. Python software

9.4.4. SAR change detection on the Google Earth Engine

9.5. Radiometric normalization of visual/infra-red images

9.5.1. Scatter plot matching

9.5.2. Automatic radiometric normalization

9.6. Exercises

A. Mathematical Tools

A.1. Cholesky decomposition

A.2. Vector and inner product spaces

A.3. Complex numbers, vectors, and matrices

A.4. Least squares procedures

A.4.1. Recursive linear regression

A.4.2. Orthogonal linear regression

A.5. Proof of Theorem 7.1

B. Neural Network Training Algorithms

B.1. The Hessian matrix

B.1.1. The R-operator

B.1.2. Calculating the Hessian

B.2. Scaled conjugate gradient training

B.2.1. Conjugate directions

B.2.2. Minimizing a quadratic function

B.2.3. The algorithm

B.3. Extended Kalman filter training

B.3.1. Linearization

B.3.2. The algorithm

C. Software

C.1. Installation

C.2. Command line utilities

C.2.1. gdal

C.2.2. earthengine

C.2.3. ipcluster.

C.3. Source code and documentation

C.4. Solutions manual

Mathematical Notation

References

Index.

Notes:

Includes bibliographical references and index.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

1-04-035165-4

1-003-50328-4

1-04-035158-1

9781003503286

OCLC:

1511004327

Publisher Number:

CIPO000240643