دورية أكاديمية
FCUnet: Refined remote sensing image segmentation method based on a fuzzy deep learning conditional random field network
| العنوان: | FCUnet: Refined remote sensing image segmentation method based on a fuzzy deep learning conditional random field network |
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| المؤلفون: | Xiangyue Ma, Jindong Xu, Qiangpeng Chong, Shifeng Ou, Haihua Xing, Mengying Ni |
| المصدر: | IET Image Processing, Vol 17, Iss 12, Pp 3616-3629 (2023) |
| بيانات النشر: | Wiley, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Computer software |
| مصطلحات موضوعية: | fuzzy logic, image classification, image segmentation, neural nets, Photography, TR1-1050, Computer software, QA76.75-76.765 |
| الوصف: | Abstract Image segmentation is pivotal for the understanding of high‐resolution remote sensing images (HRRS). However, because of the inherent uncertainties in remote sensing images and the highly complex resolution of HRRS, ambiguity often occurs among some geographic entities in the segmentation process, and the fine segmentation of HRRS is not considered sufficiently for most existing segmentation methods. Therefore, in this paper, the authors propose a new collaborative neural network structure called fuzzy deep learning conditional random field network (FCUnet) to solve the refined segmentation of HRRS. First, the authors design a fuzzy U‐Net classification network to obtain effective feature information, which introduces the fuzzy logic unit into the network to process the ambiguity and uncertainty of HRRS. Then, the authors introduce the conditional random field (CRF) at the end of FCUnet to optimize the image segmentation results. Finally, the authors validated the effectiveness and superiority of their approach on three data sets. The experiment results revealed that FCUnet had better refined segmentation performance and generalization ability than state‐of‐the‐art methods. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1751-9667 1751-9659 |
| Relation: | https://doaj.org/toc/1751-9659; https://doaj.org/toc/1751-9667 |
| DOI: | 10.1049/ipr2.12870 |
| URL الوصول: | https://doaj.org/article/a91b300e5fea4b29bcefb4ec87885205 |
| رقم الأكسشن: | edsdoj.91b300e5fea4b29bcefb4ec87885205 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 17519667 17519659 |
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| DOI: | 10.1049/ipr2.12870 |