Label-Efficient Fine-Tuning for Remote Sensing Imagery Segmentation with Diffusion Models
High-resolution remote sensing imagery plays an essential role in urban management and environmental monitoring, providing detailed insights for applications ranging from land cover mapping to disaster response. Semantic segmentation methods are among the most effective techniques for comprehensive...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/15/2579 |
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| author | Yiyun Luo Jinnian Wang Jean Sequeira Xiankun Yang Dakang Wang Jiabin Liu Grekou Yao Sébastien Mavromatis |
| author_facet | Yiyun Luo Jinnian Wang Jean Sequeira Xiankun Yang Dakang Wang Jiabin Liu Grekou Yao Sébastien Mavromatis |
| author_sort | Yiyun Luo |
| collection | DOAJ |
| description | High-resolution remote sensing imagery plays an essential role in urban management and environmental monitoring, providing detailed insights for applications ranging from land cover mapping to disaster response. Semantic segmentation methods are among the most effective techniques for comprehensive land cover mapping, and they commonly employ ImageNet-based pre-training semantics. However, traditional fine-tuning processes exhibit poor transferability across different downstream tasks and require large amounts of labeled data. To address these challenges, we introduce Denoising Diffusion Probabilistic Models (DDPMs) as a generative pre-training approach for semantic features extraction in remote sensing imagery. We pre-trained a DDPM on extensive unlabeled imagery, obtaining features at multiple noise levels and resolutions. In order to integrate and optimize these features efficiently, we designed a multi-layer perceptron module with residual connections. It performs channel-wise optimization to suppress feature redundancy and refine representations. Additionally, we froze the feature extractor during fine-tuning. This strategy significantly reduces computational consumption and facilitates fast transfer and deployment across various interpretation tasks on homogeneous imagery. Our comprehensive evaluation on the sparsely labeled dataset MiniFrance-S and the fully labeled Gaofen Image Dataset achieved mean intersection over union scores of 42.7% and 66.5%, respectively, outperforming previous works. This demonstrates that our approach effectively reduces reliance on labeled imagery and increases transferability to downstream remote sensing tasks. |
| format | Article |
| id | doaj-art-4a2268a9068a47fbbc091f02bb5b742e |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-4a2268a9068a47fbbc091f02bb5b742e2025-08-20T03:04:43ZengMDPI AGRemote Sensing2072-42922025-07-011715257910.3390/rs17152579Label-Efficient Fine-Tuning for Remote Sensing Imagery Segmentation with Diffusion ModelsYiyun Luo0Jinnian Wang1Jean Sequeira2Xiankun Yang3Dakang Wang4Jiabin Liu5Grekou Yao6Sébastien Mavromatis7G-Mod, LIS, CNRS, Aix-Marseille University, 13009 Marseille, FranceInstitute of Aerospace Remote Sensing Innovations, Guangzhou University, Guangzhou 510006, China2ik Company, 13360 Marseille, FranceInstitute of Aerospace Remote Sensing Innovations, Guangzhou University, Guangzhou 510006, ChinaInstitute of Aerospace Remote Sensing Innovations, Guangzhou University, Guangzhou 510006, ChinaInstitute of Aerospace Remote Sensing Innovations, Guangzhou University, Guangzhou 510006, ChinaG-Mod, LIS, CNRS, Aix-Marseille University, 13009 Marseille, FranceG-Mod, LIS, CNRS, Aix-Marseille University, 13009 Marseille, FranceHigh-resolution remote sensing imagery plays an essential role in urban management and environmental monitoring, providing detailed insights for applications ranging from land cover mapping to disaster response. Semantic segmentation methods are among the most effective techniques for comprehensive land cover mapping, and they commonly employ ImageNet-based pre-training semantics. However, traditional fine-tuning processes exhibit poor transferability across different downstream tasks and require large amounts of labeled data. To address these challenges, we introduce Denoising Diffusion Probabilistic Models (DDPMs) as a generative pre-training approach for semantic features extraction in remote sensing imagery. We pre-trained a DDPM on extensive unlabeled imagery, obtaining features at multiple noise levels and resolutions. In order to integrate and optimize these features efficiently, we designed a multi-layer perceptron module with residual connections. It performs channel-wise optimization to suppress feature redundancy and refine representations. Additionally, we froze the feature extractor during fine-tuning. This strategy significantly reduces computational consumption and facilitates fast transfer and deployment across various interpretation tasks on homogeneous imagery. Our comprehensive evaluation on the sparsely labeled dataset MiniFrance-S and the fully labeled Gaofen Image Dataset achieved mean intersection over union scores of 42.7% and 66.5%, respectively, outperforming previous works. This demonstrates that our approach effectively reduces reliance on labeled imagery and increases transferability to downstream remote sensing tasks.https://www.mdpi.com/2072-4292/17/15/2579remote sensing imagery segmentationfine-tuningdenoising diffusion probabilistic modelself-supervised learning |
| spellingShingle | Yiyun Luo Jinnian Wang Jean Sequeira Xiankun Yang Dakang Wang Jiabin Liu Grekou Yao Sébastien Mavromatis Label-Efficient Fine-Tuning for Remote Sensing Imagery Segmentation with Diffusion Models Remote Sensing remote sensing imagery segmentation fine-tuning denoising diffusion probabilistic model self-supervised learning |
| title | Label-Efficient Fine-Tuning for Remote Sensing Imagery Segmentation with Diffusion Models |
| title_full | Label-Efficient Fine-Tuning for Remote Sensing Imagery Segmentation with Diffusion Models |
| title_fullStr | Label-Efficient Fine-Tuning for Remote Sensing Imagery Segmentation with Diffusion Models |
| title_full_unstemmed | Label-Efficient Fine-Tuning for Remote Sensing Imagery Segmentation with Diffusion Models |
| title_short | Label-Efficient Fine-Tuning for Remote Sensing Imagery Segmentation with Diffusion Models |
| title_sort | label efficient fine tuning for remote sensing imagery segmentation with diffusion models |
| topic | remote sensing imagery segmentation fine-tuning denoising diffusion probabilistic model self-supervised learning |
| url | https://www.mdpi.com/2072-4292/17/15/2579 |
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