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 unmixing, 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.