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Review | Open Access

A Review of Disentangled Representation Learning for Remote Sensing Data

Mi Wang1( )Huiwen Wang1Jing Xiao2Liang Liao3
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China
School of Computer Science, Wuhan University, Wuhan 430072, China
School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
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Abstract

Representation learning is one of the core problems in machine learning research. The transition of input representations for machine learning algorithms from handcraft features, which dominated in the past, to the potential representations learned through deep neural networks nowadays has led to tremendous improvements in algorithm performance. However, the current representations are usually highly entangled, i.e., all information components of the input data are encoded into the same feature space, thus affecting each other and making it difficult to distinguish. Disentangled representation learning aims to learn a low-dimensional interpretable abstract representation that can identify and isolate different potential variables hidden in the high-dimensional observations. Disentangled representation learning can capture information about a single change factor and control it by the corresponding potential subspace, providing a robust representation for complex changes in the data. In this paper, we first introduce and analyze the current status of research on disentangled representation and its causal mechanisms and summarize three crucial properties of disentangled representation. Then, disentangled representation learning algorithms are classified into four categories and outlined in terms of both mathematical description and applicability. Subsequently, the loss functions and objective evaluation metrics commonly used in existing work on disentangled representation are classified. Finally, the paper summarizes representative applications of disentangled representation learning in the field of remote sensing and discusses its future development.

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CAAI Artificial Intelligence Research
Pages 172-190
Cite this article:
Wang M, Wang H, Xiao J, et al. A Review of Disentangled Representation Learning for Remote Sensing Data. CAAI Artificial Intelligence Research, 2022, 1(2): 172-190. https://doi.org/10.26599/AIR.2022.9150012

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Received: 30 September 2022
Revised: 12 December 2022
Accepted: 17 January 2023
Published: 10 March 2023
© The author(s) 2022

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