Domain Adaptation for the Classification of Remote Sensing Data: An Overview of Recent Advances

By Devis Tuia, Claudio Persello, Lorenzo Bruzzone. IEEE Geoscience and Remote Sensing Magazine, 2016

565 citations recorded by the source corpus.

The success of the 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 that is 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. 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 article provides a critical review of the recent advances in DA approaches for remote sensing and presents an overview of DA methods divided into four categories: 1) invariant feature selection, 2) representation matching, 3) adaptation of classifiers, and 4) selective sampling. We provide an overview of recent methodologies, examples of applications of the considered techniques to real remote sensing images characterized by very high spatial and spectral resolution as well as possible guidelines for the selection of the method to use in real application scenarios.

Subfields: Artificial Intelligence, Media Technology

View Paper: https://www.semanticscholar.org/paper/78f13567048f213e8902d2cafc7a0de362d180cb

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Citations in the corpus

Land-cover classification with high-resolution remote sensing images using transferable deep models (2018, background): “Furthermore, diverse imaging conditions usually lead to photographic distortions, variations in scale and changes of illumination in RS images, which often seriously reduces the separability among different classes (Tuia et al., 2016).”
A Review on Deep Learning in UAV Remote Sensing (2021, methodology): “When the model is used in practice, it is common for spectral shifts to occur between the training and test images due to di ff er-ences in acquisition, geographic region, atmospheric conditions, among others [187].”
Self-Supervised Remote Sensing Feature Learning: Learning Paradigms, Challenges, and Future Works (2022, unknown): ““under-learning” problems in application [46], [47].”
RSVQA: Visual Question Answering for Remote Sensing Data (2020, methodology): “Such a method could be developed for the image space only (a review of domain adaptation for remote sensing is done in [39]) or at the question/image level (see [40] that presents a method for domain adaptation in the context of VQA).”