Mathematical Models for Remote Sensing Image Processing

Gabriele Moser received the laurea (M.Sc. equivalent) degree in telecommunications engineering, and the Ph.D. degree in space sciences and engineering from the University of Genoa, Italy, in 2001 and 2005, respectively. Since 2014, he has been an associate professor of telecommunications at the University of Genoa. Since 2001, he has cooperated with the Image Processing and Pattern Recognition for Remote Sensing laboratory of the University of Genoa. Since 2013, he has been the Head of the Remote Sensing for Environment and Sustainability laboratory at the Savona Campus of the University of Genoa. From January to March 2004, he was a visiting student at INRIA, Sophia Antipolis, France. In 2016, he spent a period as visiting professor at the National Polytechnic Institute of Toulouse, France. From 2012 to 2016, he was an external collaborator of the Ayin laboratory at INRIA, Sophia Antipolis, France. He is co-author of more than 150 scientific publications in journals, international edited books, and conference proceedings. His research activity is focused on pattern recognition and image processing methodologies for remote sensing and energy applications.

Mathematical models and methods for remote sensing image analysis: an introduction.- Models for hyperspectral image analysis: from unmixing to object-based classification.- Very high spatial resolution optical imagery: tree-based methods and multi-temporal models for mining and analysis.- Very high resolution and interferometric SAR: Markovian and patch-based non-local mathematical models.- Polarimetric SAR modelling: Mellin kind statistics and time-frequency analysis.- Remote sensing data fusion: guided filter-based hyperspectral pansharpening and graph-based feature-level fusion.- "Remote sensing data fusion: Markov models and mathematical morphology for multisensor, multiresolution, and multiscale image classification.- Change detection in multitemporal images through single-and multi-scale approaches.- Satellite image time series: mathematical models for data mining and missing data restoration.-  Advances in kernel machines for image classification and biophysical parameter retrieval.

"The book provides a comprehensive picture of the advances in the field of mathematical models in 2D remote sensing image analysis, which is ideal as a reference resource for remote sensing scientists, practitioners, as well as graduate and doctoral students. ... The book is presented in a lucid manner, starting with the basics and using unified notation throughout. It is therefore self-contained enough to help beginners, too." (Zoltan Kato, IAPR Newsletter, Vol. 41 (3), July, 2019)