Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL and Python, Third Edition, 3rd Edition (Hardback) book cover

Image Analysis, Classification and Change Detection in Remote Sensing

With Algorithms for ENVI/IDL and Python, Third Edition, 3rd Edition

By Morton John Canty

CRC Press

576 pages | 143 B/W Illus.

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pub: 2014-06-06
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Description

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:

  • 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.

Reviews

"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."

—Gunter Menz,

Table of Contents

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

Exercises

Image Statistics

Random variables

Parameter estimation

Multivariate distributions

Bayes’ Theorem, likelihood and classification

Hypothesis testing

Ordinary linear regression

Entropy and information

Exercises

Transformations

The discrete Fourier transform

The discrete wavelet transform

Principal components

Minimum noise fraction

Spatial correlation

Exercises

Filters, Kernels and Fields

The Convolution Theorem

Linear filters

Wavelets and filter banks

Kernel methods

Gibbs–Markov random fields

Exercises

Image Enhancement and Correction

Lookup tables and histogram functions

High-pass spatial filtering and feature extraction

Panchromatic sharpening

Radiometric correction of polarimetric SAR imagery

Topographic correction

Image–image registration

Exercises

Supervised Classification Part

Maximizing the a posteriori probability

Training data and separability

Maximum likelihood classification

Gaussian kernel classification

Neural networks

Support vector machines

Exercises

Supervised Classification Part

Postprocessing

Evaluation and comparison of classification accuracy

Adaptive boosting

Classification of polarimetric SAR imagery

Hyperspectral image analysis

Exercises

Unsupervised Classification

Simple cost functions

Algorithms that minimize the simple cost functions

Gaussian mixture clustering

Including spatial information

A benchmark

The Kohonen self-organizing map

Image segmentation

Exercises

Change Detection

Algebraic methods

Postclassification comparison

Principal components analysis (PCA)

Multivariate alteration detection (MAD)

Decision thresholds

Unsupervised change classification

Change detection with polarimetric SAR imagery

Radiometric normalization of multispectral imagery

Exercises

A Mathematical Tools

B Efficient Neural Network Training Algorithms

C ENVI Extensions in IDL

D Python Scripts

Mathematical Notation

References

Index

Subject Categories

BISAC Subject Codes/Headings:
MAT004000
MATHEMATICS / Arithmetic
TEC015000
TECHNOLOGY & ENGINEERING / Imaging Systems
TEC036000
TECHNOLOGY & ENGINEERING / Remote Sensing & Geographic Information Systems