Change Detection in Remote Sensing: From Classical Methods to Deep Learning Systems is a full textbook that shows how to use both old and new analytical methods to look at Earth observation data over time.

The book is for students, researchers, and professionals. It starts with the basics of remote sensing and digital image processing and goes all the way up to advanced AI-powered change detection systems. It starts with basic math and stats and then goes on to more advanced methods like image differencing, Change Vector Analysis (CVA), PCA, and MAD. Lastly, it talks about machine learning models such as SVMs, Random Forests, and ensemble methods. The book goes into more depth about deep learning architectures like U-Net, Siamese networks, Transformers, and hybrid CNN-Transformer models. It also talks about self-supervised and generative methods.

The book is more about systems than just algorithms. It talks about things like big computational pipelines, cloud and edge computing, GPU acceleration, benchmark datasets, evaluation metrics, and case studies of real-world deployments. It talks about how to use these tools for urban monitoring, farming, studying the environment, figuring out what happened during a disaster, and analyzing the climate.

This book is both a core textbook and a directed study guide for modern geospatial AI and remote sensing change detection. It has a lot of references, clear explanations, and tough math.