Unique Perturbation Methods Exploitation for Semi-Supervised Remote Sensing Image Semantic Segmentation
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| Publicado en: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences vol. X-G-2025 (2025), p. 1085 |
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| Autor principal: | |
| Otros Autores: | , , , , , |
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Copernicus GmbH
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| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| 022 | |a 2194-9042 | ||
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| 024 | 7 | |a 10.5194/isprs-annals-X-G-2025-1085-2025 |2 doi | |
| 035 | |a 3243894528 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 263032 |2 nlm | ||
| 100 | 1 | |a Zhou, Liang |u Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China; Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China | |
| 245 | 1 | |a Unique Perturbation Methods Exploitation for Semi-Supervised Remote Sensing Image Semantic Segmentation | |
| 260 | |b Copernicus GmbH |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Deep learning has significantly improved the accuracy of remote sensing semantic segmentation, yet its effectiveness is often constrained by the limited availability of annotated training samples. Semi-supervised learning (SSL) addresses this challenge by utilizing abundant unlabeled data, reducing dependence on manual annotations. However, current consistency regularization-based SSL methods, primarily developed for natural images, struggle to produce adequate perturbation diversity for robust model training in remote sensing image segmentation. In this work, we propose FusionMatch, a novel SSL framework featuring two perturbation mechanisms - NIRPerb and PSPerb - specifically designed for remote sensing imagery. NIRPerb utilizes near-infrared spectral data to enhance perturbation diversity. PSPerb adopts differentiated pan-sharpening fusion strategies to expand the perturbation space. Extensive experiments on both a building extraction dataset and a multi-class dataset demonstrate that FusionMatch outperforms state-of-the-art SSL methods in segmentation accuracy and robustness. | |
| 653 | |a Regularization | ||
| 653 | |a Datasets | ||
| 653 | |a Image segmentation | ||
| 653 | |a Near infrared radiation | ||
| 653 | |a Remote sensing | ||
| 653 | |a Infrared imagery | ||
| 653 | |a Semantic segmentation | ||
| 653 | |a Training | ||
| 653 | |a Semi-supervised learning | ||
| 653 | |a Deep learning | ||
| 653 | |a Perturbation methods | ||
| 653 | |a Infrared spectra | ||
| 653 | |a Peer review | ||
| 653 | |a Photogrammetry | ||
| 653 | |a Supervision | ||
| 653 | |a Regularization methods | ||
| 653 | |a Architecture | ||
| 653 | |a Annotations | ||
| 653 | |a Semantics | ||
| 653 | |a Information science | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Duan, Keyi |u Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China; Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China | |
| 700 | 1 | |a Dai, Jinkun |u Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China; Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China | |
| 700 | 1 | |a Wu, Xiaodan |u Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China; Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China | |
| 700 | 1 | |a Ge, Xuming |u Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China; Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China | |
| 700 | 1 | |a Li, Xiaojun |u Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China; Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China | |
| 700 | 1 | |a Ye, Yuanxin |u Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China; Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China | |
| 773 | 0 | |t ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |g vol. X-G-2025 (2025), p. 1085 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3243894528/abstract/embedded/CH9WPLCLQHQD1J4S?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3243894528/fulltextPDF/embedded/CH9WPLCLQHQD1J4S?source=fedsrch |