Machine Learning-Based Hyperspectral Image Processing

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.