DiffRSS: A Diffusion-Guided Multi-Scale Features Remote Sensing Image Segmentation Method
Semantic segmentation in remote sensing is a fundamental task with crucial applications across various domains. Traditional approaches primarily utilize bottom-up discriminative methods, where network architectures learn image features to generate segmentation masks. However, the complexity of remot...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10816160/ |
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| author | Honghao Liu Ruixia Yang Yue Xu Zhengchao Chen Yuyang Zheng |
| author_facet | Honghao Liu Ruixia Yang Yue Xu Zhengchao Chen Yuyang Zheng |
| author_sort | Honghao Liu |
| collection | DOAJ |
| description | Semantic segmentation in remote sensing is a fundamental task with crucial applications across various domains. Traditional approaches primarily utilize bottom-up discriminative methods, where network architectures learn image features to generate segmentation masks. However, the complexity of remote sensing images, characterized by diverse ground object types and intricate scenes, often results in information redundancy and confusion during feature extraction, impacting segmentation accuracy. To address these challenges, we introduce a novel segmentation framework, DiffRSS, based on the denoising model paradigm. This top-down generative approach learns the data distribution of sample labels and uses image features as guiding priors for generating segmentation masks. We conceptualize the semantic segmentation of remote sensing images as a conditional generation task and design a Multiscale Cyclic Denoising Module (MSCDM), which effectively leverages multiscale features of remote sensing images, leading to superior segmentation outcomes. Inspired by diffusion models, our denoising structure, MSCDM, can be reused multiple times during inference, enhancing the quality of segmentation masks. This method allows for more precise capture and utilization of image features, resulting in finer and more accurate segmentation masks. Extensive testing on three public remote sensing datasets the ISPRS Vaihingen, ISPRS Potsdam, and GID Fine Land Cover Classification datasets demonstrates that our method achieves competitive results. |
| format | Article |
| id | doaj-art-1bee7033e4e54a9aa64269268da75b26 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-1bee7033e4e54a9aa64269268da75b262025-01-30T00:03:10ZengIEEEIEEE Access2169-35362025-01-011380281610.1109/ACCESS.2024.352228610816160DiffRSS: A Diffusion-Guided Multi-Scale Features Remote Sensing Image Segmentation MethodHonghao Liu0https://orcid.org/0009-0001-1830-0883Ruixia Yang1Yue Xu2https://orcid.org/0000-0001-6683-2107Zhengchao Chen3https://orcid.org/0000-0003-4293-6459Yuyang Zheng4https://orcid.org/0009-0007-6006-6510Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaSemantic segmentation in remote sensing is a fundamental task with crucial applications across various domains. Traditional approaches primarily utilize bottom-up discriminative methods, where network architectures learn image features to generate segmentation masks. However, the complexity of remote sensing images, characterized by diverse ground object types and intricate scenes, often results in information redundancy and confusion during feature extraction, impacting segmentation accuracy. To address these challenges, we introduce a novel segmentation framework, DiffRSS, based on the denoising model paradigm. This top-down generative approach learns the data distribution of sample labels and uses image features as guiding priors for generating segmentation masks. We conceptualize the semantic segmentation of remote sensing images as a conditional generation task and design a Multiscale Cyclic Denoising Module (MSCDM), which effectively leverages multiscale features of remote sensing images, leading to superior segmentation outcomes. Inspired by diffusion models, our denoising structure, MSCDM, can be reused multiple times during inference, enhancing the quality of segmentation masks. This method allows for more precise capture and utilization of image features, resulting in finer and more accurate segmentation masks. Extensive testing on three public remote sensing datasets the ISPRS Vaihingen, ISPRS Potsdam, and GID Fine Land Cover Classification datasets demonstrates that our method achieves competitive results.https://ieeexplore.ieee.org/document/10816160/Remote sensingsemantic segmentationdiffusion model |
| spellingShingle | Honghao Liu Ruixia Yang Yue Xu Zhengchao Chen Yuyang Zheng DiffRSS: A Diffusion-Guided Multi-Scale Features Remote Sensing Image Segmentation Method IEEE Access Remote sensing semantic segmentation diffusion model |
| title | DiffRSS: A Diffusion-Guided Multi-Scale Features Remote Sensing Image Segmentation Method |
| title_full | DiffRSS: A Diffusion-Guided Multi-Scale Features Remote Sensing Image Segmentation Method |
| title_fullStr | DiffRSS: A Diffusion-Guided Multi-Scale Features Remote Sensing Image Segmentation Method |
| title_full_unstemmed | DiffRSS: A Diffusion-Guided Multi-Scale Features Remote Sensing Image Segmentation Method |
| title_short | DiffRSS: A Diffusion-Guided Multi-Scale Features Remote Sensing Image Segmentation Method |
| title_sort | diffrss a diffusion guided multi scale features remote sensing image segmentation method |
| topic | Remote sensing semantic segmentation diffusion model |
| url | https://ieeexplore.ieee.org/document/10816160/ |
| work_keys_str_mv | AT honghaoliu diffrssadiffusionguidedmultiscalefeaturesremotesensingimagesegmentationmethod AT ruixiayang diffrssadiffusionguidedmultiscalefeaturesremotesensingimagesegmentationmethod AT yuexu diffrssadiffusionguidedmultiscalefeaturesremotesensingimagesegmentationmethod AT zhengchaochen diffrssadiffusionguidedmultiscalefeaturesremotesensingimagesegmentationmethod AT yuyangzheng diffrssadiffusionguidedmultiscalefeaturesremotesensingimagesegmentationmethod |