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Joint-Optimized Unsupervised Adversarial Domain Adaptation in Remote Sensing Segmentation with Prompted Foundation Model

arXiv 2411.05878 ↗First posted 8 Nov 2024cs.CVcited by 0

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Abstract (arXiv)

Unsupervised Domain Adaptation for Remote Sensing Semantic Segmentation (UDA-RSSeg) addresses the challenge of adapting a model trained on source domain data to target domain samples, thereby minimizing the need for annotated data across diverse remote sensing scenes. This task presents two principal challenges: (1) severe inconsistencies in feature representation across different remote sensing domains, and (2) a domain gap that emerges due to the representation bias of source domain patterns when translating features to predictive logits. To tackle these issues, we propose a joint-optimized adversarial network incorporating the "Segment Anything Model (SAM) (SAM-JOANet)" for UDA-RSSeg. Our approach integrates SAM to leverage its robust generalized representation capabilities, thereby alleviating feature inconsistencies. We introduce a finetuning decoder designed to convert SAM-Encoder features into predictive logits. Additionally, a feature-level adversarial-based prompted segmentor is employed to generate class-agnostic maps, which guide the finetuning decoder's feature representations. The network is optimized end-to-end, combining the prompted segmentor and the finetuning decoder. Extensive evaluations on benchmark datasets, including ISPRS (Potsdam/Vaihingen) and CITY-OSM (Paris/Chicago), demonstrate the effectiveness of our method. The results, supported by visualization and analysis, confirm the method's interpretability and robustness. The code of this paper is available at https://github.com/CV-ShuchangLyu/SAM-JOANet.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 29 of its 56 references that are in the map. First posted on arXiv 8 Nov 2024.

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29 of 56 references.

References · 29

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
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  12. 2018Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptationcited by 295
17 more
  1. 2020BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentationcited by 294
  2. 2021Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentationcited by 268
  3. 2021Self-supervised Augmentation Consistency for Adapting Semantic Segmentationcited by 149
  4. 2019Domain Adaptation for Semantic Segmentation with Maximum Squares Losscited by 138
  5. 2020Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentationcited by 126
  6. 2023SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Modelcited by 38
  7. 2019Unsupervised Domain Adaptation using Generative Adversarial Networks for Semantic Segmentation of Aerial Imagescited by 32
  8. 2017Learning Aerial Image Segmentation from Online Mapscited by 30
  9. 2019ColorMapGAN: Unsupervised Domain Adaptation for Semantic Segmentation Using Color Mapping Generative Adversarial Networkscited by 26
  10. 2021Dual Path Learning for Domain Adaptation of Semantic Segmentationcited by 26
  11. 2021Stagewise Unsupervised Domain Adaptation with Adversarial Self-Training for Road Segmentation of Remote Sensing Imagescited by 20
  12. 2018Dynamic Multi-Context Segmentation of Remote Sensing Images based on Convolutional Networkscited by 15
  13. 2022Curriculum-style Local-to-global Adaptation for Cross-domain Remote Sensing Image Segmentationcited by 11
  14. 2023Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel Descriptorscited by 8
  15. 2023Self-Training Guided Disentangled Adaptation for Cross-Domain Remote Sensing Image Semantic Segmentationcited by 7
  16. 2023Implicit Ray-Transformers for Multi-view Remote Sensing Image Segmentationcited by 6
  17. 2023Leveraging Topology for Domain Adaptive Road Segmentation in Satellite and Aerial Imagerycited by 1

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