576 pages | 143 B/W Illus.
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 What’s New in the Third Edition:
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.
"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 Canty’s 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."
Images, Arrays, and Matrices
Multispectral satellite images
Synthetic aperture radar images
Algebra of vectors and matrices
Eigenvalues and eigenvectors
Singular value decomposition
Finding minima and maxima
Bayes’ Theorem, likelihood and classification
Ordinary linear regression
Entropy and information
The discrete Fourier transform
The discrete wavelet transform
Minimum noise fraction
Filters, Kernels and Fields
The Convolution Theorem
Wavelets and filter banks
Gibbs–Markov random fields
Image Enhancement and Correction
Lookup tables and histogram functions
High-pass spatial filtering and feature extraction
Radiometric correction of polarimetric SAR imagery
Supervised Classification Part
Maximizing the a posteriori probability
Training data and separability
Maximum likelihood classification
Gaussian kernel classification
Support vector machines
Supervised Classification Part
Evaluation and comparison of classification accuracy
Classification of polarimetric SAR imagery
Hyperspectral image analysis
Simple cost functions
Algorithms that minimize the simple cost functions
Gaussian mixture clustering
Including spatial information
The Kohonen self-organizing map
Principal components analysis (PCA)
Multivariate alteration detection (MAD)
Unsupervised change classification
Change detection with polarimetric SAR imagery
Radiometric normalization of multispectral imagery
A Mathematical Tools
B Efficient Neural Network Training Algorithms
C ENVI Extensions in IDL
D Python Scripts