Methodological Advances in Remote Sensing Image Processing

Abstract

Remote sensing image processing is nowadays a mature research area. The techniques developed in the field allow many real-life
applications with great societal value. For instance, urban monitoring, fire detection or flood prediction can have a great impact on economical
and environmental issues.
To attain such objectives, the remote sensing community has turned into a multidisciplinary field of science that embraces physics, signal theory,
computer science, electronics, and communications.
From a machine learning and signal/image processing point of view, all the applications are tackled under specific formalisms, such as
classification and clustering, regression and function approximation, image coding, restoration and enhancement, source un mixing, data fusion
or feature selection and extraction.
This paper serves as a survey of methods and applications, and reviews the last methodological advances in remote sensing image processing.

Index Terms— Remote sensing, machine learning, signal and image processing, survey, applications.