mlsanityData to 7 Oct 2026

Integrating Multiple Sources Knowledge for Class Asymmetry Domain Adaptation Segmentation of Remote Sensing Images

arXiv 2305.09893 ↗First posted 17 May 2023cs.CVcited by 6

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Abstract (arXiv)

In the existing unsupervised domain adaptation (UDA) methods for remote sensing images (RSIs) semantic segmentation, class symmetry is an widely followed ideal assumption, where the source and target RSIs have exactly the same class space. In practice, however, it is often very difficult to find a source RSI with exactly the same classes as the target RSI. More commonly, there are multiple source RSIs available. To this end, a novel class asymmetry RSIs domain adaptation method with multiple sources is proposed in this paper, which consists of four key components. Firstly, a multi-branch segmentation network is built to learn an expert for each source RSI. Secondly, a novel collaborative learning method with the cross-domain mixing strategy is proposed, to supplement the class information for each source while achieving the domain adaptation of each source-target pair. Thirdly, a pseudo-label generation strategy is proposed to effectively combine strengths of different experts, which can be flexibly applied to two cases where the source class union is equal to or includes the target class set. Fourthly, a multiview-enhanced knowledge integration module is developed for the high-level knowledge routing and transfer from multiple domains to target predictions.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 28 of its 77 references that are in the map. First posted on arXiv 17 May 2023.

Builds on

28 of 77 references.

References · 28

  1. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,875
  2. 2018ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentationcited by 623
  3. 2021DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentationcited by 299
  4. 2018Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptationcited by 295
  5. 2021Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentationcited by 268
  6. 2021LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentationcited by 259
  7. 2019Hypergraph Convolution and Hypergraph Attentioncited by 226
  8. 2020ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learningcited by 209
  9. 2022HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentationcited by 163
  10. 2020Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentationcited by 142
  11. 2022UIU-Net: U-Net in U-Net for Infrared Small Object Detectioncited by 124
  12. 2018Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shiftcited by 109
16 more
  1. 2022SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentationcited by 87
  2. 2017Partial Transfer Learning with Selective Adversarial Networkscited by 84
  3. 2019Multi-source Domain Adaptation for Semantic Segmentationcited by 74
  4. 2018An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixingcited by 65
  5. 2022Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic Segmentationcited by 47
  6. 2021Multi-Source Domain Adaptation with Collaborative Learning for Semantic Segmentationcited by 44
  7. 2021Looking Outside the Window: Wide-Context Transformer for the Semantic Segmentation of High-Resolution Remote Sensing Imagescited by 27
  8. 2019ColorMapGAN: Unsupervised Domain Adaptation for Semantic Segmentation Using Color Mapping Generative Adversarial Networkscited by 26
  9. 2020Select, Label, and Mix: Learning Discriminative Invariant Feature Representations for Partial Domain Adaptationcited by 16
  10. 2022Unsupervised domain adaptation semantic segmentation of high-resolution remote sensing imagery with invariant domain-level prototype memorycited by 15
  11. 2020DAugNet: Unsupervised, Multi-source, Multi-target, and Life-long Domain Adaptation for Semantic Segmentation of Satellite Imagescited by 13
  12. 2022From Big to Small: Adaptive Learning to Partial-Set Domainscited by 9
  13. 2022A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images-Analysis Unit,Model Scalability and Transferabilitycited by 4
  14. 2022High-resolution semantically-consistent image-to-image translationcited by 3
  15. 2022Union-set Multi-source Model Adaptation for Semantic Segmentationcited by 3
  16. 2022Depth-Assisted ResiDualGAN for Cross-Domain Aerial Images Semantic Segmentationcited by 1

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Cited by year

Citing papers per year: 2024: 2; 2025: 4.

6 papers.

  1. 2024Diff-Mosaic: Augmenting Realistic Representations in Infrared Small Target Detection via Diffusion Priorcited by 8
  2. 2024Multi-source Domain Adaptation for Panoramic Semantic Segmentationcited by 7
  3. 2025Deep Learning Based Domain Adaptation Methods in Remote Sensing: A Comprehensive Surveycited by 2
  4. 2025Sampling Innovation-Based Adaptive Compressive Sensingcited by 1
  5. 2025SETrLUSI: Stochastic Ensemble Multi-Source Transfer Learning Using Statistical Invariantcited by 0
  6. 2025Prototype-Based Pseudo-Label Denoising for Source-Free Domain Adaptation in Remote Sensing Semantic Segmentationcited by 0