Spatial Information-guided Diffusion for Domain Adaptation Semantic Segmentation of Remote Sensing Images

LIANG Yan; LI Jun-Fan; SHAO Kai; HU Lin

doi:10.11999/JEIT260031

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LIANG Yan, LI Jun-Fan, SHAO Kai, HU Lin. Spatial Information-guided Diffusion for Domain Adaptation Semantic Segmentation of Remote Sensing Images[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260031

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LIANG Yan, LI Jun-Fan, SHAO Kai, HU Lin. Spatial Information-guided Diffusion for Domain Adaptation Semantic Segmentation of Remote Sensing Images[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260031

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Spatial Information-guided Diffusion for Domain Adaptation Semantic Segmentation of Remote Sensing Images

doi: 10.11999/JEIT260031 cstr: 32379.14.JEIT260031

LIANG Yan^{1, 2
,
,},
LI Jun-Fan^{1, 2
,},
SHAO Kai¹,
HU Lin¹

1.
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Chongqing Key Laboratory of Signal and Information Processing, Chongqing 400065, China

Funds: The Natural Science Foundation of Chongqing (CSTB2025NSCQ-GPX1253)

Received Date: 2026-01-09
Accepted Date: 2026-04-09
Rev Recd Date: 2026-04-03

Available Online: 2026-04-27

Abstract

Abstract

Objective Domain Adaptation Semantic Segmentation (DASS) is critical for remote sensing applications, including land-cover mapping, urban planning, and environmental monitoring. However, deep learning models often show severe performance degradation under domain shifts caused by imaging variation, geographic differences, and label-semantic heterogeneity. Conventional feature-alignment and generative adversarial network-based methods often fail to preserve semantic consistency. They are also sensitive to noisy supervision, especially when cross-domain gaps are large. This work aims to construct a robust DASS framework for semantically consistent image translation and reliable knowledge transfer. Methods A two-stage framework, termed Co-training Spatial-Guided DASS (CoSG-DASS), is proposed by integrating image translation and co-training. In the image-translation stage, a spatial information-guided latent diffusion model enhanced by ControlNet is designed. Semantic pseudo-labels and depth estimates are used as horizontal semantic and vertical spatial conditions to guide target-style image generation. To reduce the effect of noisy pseudo-labels, an Entropy-based Adaptive Guidance Intensity Module (EAGIM) is introduced. EAGIM estimates pixel-level confidence using information entropy and suppresses unreliable features. In the co-training stage, translated target-style images and unlabeled real target-domain images are used to train a segmentation model with a depth-guided segmentation head. Cross-entropy loss and adversarial loss are jointly used for optimization. Results and Discussions Extensive experiments are conducted on three cross-domain tasks. CoSG-DASS generates images that better match target-domain distributions. Quantitative results based on Fréchet Inception Distance (FID) show that the proposed method outperforms CycleGAN, UNI-Diff, and CRS-Diff in most settings (Table 1). Visual comparisons (Fig. 6) show that the method reduces edge blurring and category confusion. It also improves the separation of roads and vegetation and preserves small objects, such as vehicles. In the semantic segmentation stage, CoSG-DASS outperforms state-of-the-art domain adaptation methods. It improves mean Intersection over Union (mIoU) by 1.14%, 3.78%, and 2.49% on the cross-geographic task (Vaihingen IRRG→Potsdam IRRG), cross-imaging-mode task (Vaihingen IRRG→Potsdam RGB), and bidirectional label-semantic-heterogeneity tasks between DFC25 and LoveDA, respectively (Tables 2～4). Visual segmentation results (Fig. 7) confirm its strong boundary preservation and high accuracy in complex scenes. Ablation studies (Table 5) verify the contribution of the core components, including depth control, pseudo-label guidance, EAGIM, and the co-training strategy. Feature-distribution visualization based on Uniform Manifold Approximation and Projection (UMAP) further shows that CoSG-DASS reduces intra-class variation and increases inter-class separation after adaptation (Fig. 8). Conclusions CoSG-DASS alleviates domain shifts in remote sensing images through semantic-preserving diffusion-based translation and depth-guided co-training. It improves both image-translation quality and segmentation accuracy over existing methods. The proposed framework provides an effective solution for multi-source remote sensing interpretation. Future work will focus on extreme label-semantic heterogeneity and lightweight diffusion architectures.
- Diffusion models,
- Domain adaptation,
- Semantic segmentation,
- Remote sensing images,
- Information entropy

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References(27)

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