Deep Learning Based Domain Adaptation Methods in Remote Sensing: A Comprehensive Survey

2025-10-17

AI Summary

Remote sensing domain adaptation in deep learning confronts distribution shifts between source and target domains arising from sensor heterogeneity, imaging conditions, and geographical variations. This paper presents a systematic survey of the field and introduces the first remote sensing–specific multidimensional taxonomy—categorizing methods by task type, input modality, and supervision paradigm—thereby substantially broadening coverage and structural rigor beyond prior surveys. Grounded in deep feature alignment and cross-domain knowledge transfer, the work unifies major benchmark datasets, standardized evaluation protocols, and a multi-perspective algorithmic analysis framework. Empirical evaluation synthesizes performance of state-of-the-art methods on benchmarks including Sentinel-2 and UCMerced, exposing critical bottlenecks: semantic gap, label scarcity, and limited generalization robustness. Finally, the survey identifies promising future directions, including physics-guided transfer learning, self-supervised collaborative optimization, and open-scenario adaptation.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Remote Sensing / Geospatial AI
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Domain adaptation is a crucial and increasingly important task in remote sensing, aiming to transfer knowledge from a source domain a differently distributed target domain. It has broad applications across various real-world applications, including remote sensing element interpretation, ecological environment monitoring, and urban/rural planning. However, domain adaptation in remote sensing poses significant challenges due to differences in data, such as variations in ground sampling distance, imaging modes from various sensors, geographical landscapes, and environmental conditions. In recent years, deep learning has emerged as a powerful tool for feature representation and cross-domain knowledge transfer, leading to widespread adoption in remote sensing tasks. In this paper, we present a comprehensive survey of significant advancements in deep learning based domain adaptation for remote sensing. We first introduce the preliminary knowledge to clarify key concepts, mathematical notations, and the taxonomy of methodologies. We then organize existing algorithms from multiple perspectives, including task categorization, input mode, supervision paradigm, and algorithmic granularity, providing readers with a structured understanding of the field. Next, we review widely used datasets and summarize the performance of state-of-the-art methods to provide an overview of current progress. We also identify open challenges and potential directions to guide future research in domain adaptation for remote sensing. Compared to previous surveys, this work addresses a broader range of domain adaptation tasks in remote sensing, rather than concentrating on a few subfields. It also presents a systematic taxonomy, providing a more comprehensive and organized understanding of the field. As a whole, this survey can inspire the research community, foster understanding, and guide future work in the field.

Problem

• Addressing domain shift challenges in remote sensing data analysis
• Surveying deep learning methods for cross-domain knowledge transfer
• Providing systematic taxonomy for remote sensing domain adaptation tasks

Innovation

• Deep learning for cross-domain knowledge transfer
• Systematic taxonomy of domain adaptation methods
• Comprehensive survey across multiple remote sensing tasks