Blind source separation is a relatively new signal processing method combining artificial neural networks, statistical information processing and information theory. It has tremendous potential in applications such as processing of speech, image, and biomedical signals. The technique excels in signal extraction, enhancement, denoising, model reduction and classification problems.

This book provides an overview of the basics of blind source separation along with important solutions and algorithms. Applications are also covered in-depth, including image feature extraction, remote sensing image fusion, mixed-pixel decomposition of SAR images, image object recognition, fMRI medical image processing, geochemical and geophysical data mining, mineral resources prediction and geo-anomalies information recognition. Given the multidisciplinary nature of the subject the book has been written in an accessible style so as to appeal to readers from very different backgrounds.

¿ Gives a systematic exploration of both classic and contemporary algorithms in blind source separation with practical case  studies

¿ Written by an expert team with innovations in blind source separation and its applications in natural science

¿ Codes for most of the algorithms mentioned in the book available from the author

 This book is aimed at graduate students and researchers engaged in the areas of
signal processing, data mining, image processing and recognition, computational
geosciences, computational life sciences, and other field sciences.