Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for Python

by Morton John Canty

Overview

The fifth edition of this core textbook in advanced remote sensing continues to maintain its emphasis on statistically motivated, data-driven techniques for remote sensing image analysis. The theoretical substance remains essentially the same, with new material on convolutional neural networks, transfer learning, image segmentation, random forests, and an extended implementation of sequential change detection with radar satellites. The tools which apply the algorithms to real remote sensing data are brought thoroughly up to date. As these software tools have evolved substantially with time, the fifth edition replaces the now obsolete Python 2 with Python 3 and takes advantage of the high-level packages that are based on it, such as Colab, TensorFlow/KERAS, Scikit-Learn, and the Google Earth Engine Python API.

New in the Fifth Edition:

• Thoroughly revised to include the updates needed in all chapters because of the necessary changes to the software.
• Replaces Python 2 with Python 3 tools and updates all associated subroutines, Jupyter notebooks and Python scripts.
• Presents easy, platform-independent software installation methods with Docker containers.
• Each chapter concludes with exercises complementing or extending the material in the text.
• Utilizes freely accessible imagery via the Google Earth Engine and provides many examples of cloud programming (Google Earth Engine API).
• Examines deep learning examples including TensorFlow and a sound introduction to neural networks.

This new text is essential for all upper-level undergraduate and graduate students pursuing degrees in Geography, Geology, Geophysics, Environmental Sciences and Engineering, Urban Planning, and the many subdisciplines that include advanced courses in remote sensing. It is also a great resource for researchers and scientists interested in learning techniques and technologies for collecting, analyzing, managing, processing, and visualizing geospatial datasets.

Product Details

ISBN-13:	9781040351659
Publisher:	CRC Press
Publication date:	06/03/2025
Sold by:	Barnes & Noble
Format:	eBook
Pages:	536
File size:	28 MB
Note:	This product may take a few minutes to download.

About the Author

Morton John Canty, now semi-retired, was a senior research scientist in the Institute for Bio- and Geosciences at the Jülich Research Center in Germany. He received his PhD in Nuclear Physics in 1969 at the University of Manitoba, Canada and, after post-doctoral positions in Bonn, Groningen and Marburg, began work in Jülich in 1979. There, his principal interests have been the development of statistical and game-theoretical models for the verification of international treaties and the use of remote sensing data for monitoring global treaty compliance. He has served on numerous advisory bodies to the German Federal Government and to the International Atomic Energy Agency in Vienna and was a coordinator within the European Network of Excellence on Global Monitoring for Security and Stability, funded by the European Commission. Morton Canty is the author of three monographs in the German language: on the subject of non-linear dynamics (Chaos und Systeme, Vieweg, 1995), neural networks for classification of remote sensing data (Fernerkundung mit neuronalen Netzen, Expert, 1999), and algorithmic game theory (Konfliktlösungen mit Mathematica, Springer 2000). The latter text has appeared in a revised English version (Resolving Conflicts with Mathematica, Academic Press, 2003). He is co-author of a monograph on mathematical methods for treaty verification (Compliance Quantified, Cambridge University Press, 1996). He has published many papers on the subjects of experimental nuclear physics, nuclear safeguards, applied game theory, and remote sensing and has lectured on nonlinear dynamical growth models and remote sensing digital image analysis at Universities in Bonn, Berlin, Freiberg/Saxony, and Rome.

Table of Contents

1. Images, Arrays, and Matrices. 2. Image Statistics. 3. Transformations. 4. Filters, Kernels, and Fields. 5. Image Enhancement and Correction. 6. Supervised Classification Part 1. 7. Supervised Classification Part 2. 8. Unsupervised Classification. 9. Change Detection. Appendix A: Mathematical Tools. Appendix B: Neural Network Training Algorithms. Appendix C: Software.