An authoritative deep dive into the most recent machine learning approaches to hyperspectral remote sensing image processing

In Machine-Learning-Based Hyperspectral Image Processing, a team of distinguished researchers led by Dr. Bing Zhang delivers an up-to-date discussion of machine learning-based approaches to hyperspectral image analysis. The contributors comprehensively review machine learning approaches to hyperspectral image denoising and super-resolution tasks, offering coverage of a variety of perspectives.

The book also explores the most recent research on machine learning hyperspectral unmixing methods and hyperspectral image classification. It explains the algorithms used for hyperspectral image target and change detection, as well.

Readers will also find:

• A thorough introduction to the novel concept of applying advanced machine learning techniques to the analysis of hyperspectral imagery
• Comprehensive explorations of the most recent developments in this technology and its applications
• Practical discussions of how to effectively process and extract valuable insights from hyperspectral data
• Complete treatments of a variety of hyperspectral remote sensing image processing tasks, including classification, target detection, and change detection.

Perfect for postgraduate students and research scientists with an interest in the subject, Machine-Learning-Based Hyperspectral Image Processing will also benefit researchers, academicians, and students engaged in machine learning-based approaches to image analysis.

Bing Zhang, PhD, is Full Professor and Deputy Director of the Aerospace Information Research Institute, CAS. He has authored over 300 publications and currently serves as the Chief Editor for the Chinese Journal of Remote Sensing and Associate Editor for the IEEE Transactions on Geoscience and Remote Sensing.