  The success of supervised classification of remotely sensed images acquired
over large geographical areas or at short time intervals strongly depends on
the representativity of the samples used to train the classification algorithm
and to define the model. When training samples are collected from an image (or
a spatial region) different from the one used for mapping, spectral shifts
between the two distributions are likely to make the model fail. Such shifts
are generally due to differences in acquisition and atmospheric conditions or
to changes in the nature of the object observed. In order to design
classification methods that are robust to data-set shifts, recent remote
sensing literature has considered solutions based on domain adaptation (DA)
approaches. Inspired by machine learning literature, several DA methods have
been proposed to solve specific problems in remote sensing data classification.
This paper provides a critical review of the recent advances in DA for remote
sensing and presents an overview of methods divided into four categories: i)
invariant feature selection; ii) representation matching; iii) adaptation of
classifiers and iv) selective sampling. We provide an overview of recent
methodologies, as well as examples of application of the considered techniques
to real remote sensing images characterized by very high spatial and spectral
resolution. Finally, we propose guidelines to the selection of the method to
use in real application scenarios.
