دورية أكاديمية
Fuzzy neighbourhood neural network for high-resolution remote sensing image segmentation
| العنوان: | Fuzzy neighbourhood neural network for high-resolution remote sensing image segmentation |
|---|---|
| المؤلفون: | Tingting Qu, Jindong Xu, Qianpeng Chong, Zhaowei Liu, Weiqing Yan, Xuan Wang, Yongchao Song, Mengying Ni |
| المصدر: | European Journal of Remote Sensing, Vol 56, Iss 1 (2023) |
| بيانات النشر: | Taylor & Francis Group, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Oceanography LCC:Geology |
| مصطلحات موضوعية: | Fuzzy neighbourhood, high-resolution remote sensing image, image segmentation, multi-attention gating, Oceanography, GC1-1581, Geology, QE1-996.5 |
| الوصف: | ABSTRACTRemote sensing image segmentation plays an important role in many industrial-grade image processing applications. However, the problem of uncertainty caused by intraclass heterogeneity and interclass blurring is prevalent in high-resolution remote sensing images. Moreover, the complexity of information in high-resolution remote sensing images leads to a large amount of background information around objects. To solve this problem, a new fuzzy convolutional neural network is proposed in this paper. This network resolves the ambiguity and uncertainty of feature information by introducing a fuzzy neighbourhood module in the deep learning network structure. In addition, it adds a multi-attention gating module to highlight small object features and separate them from the complex background information to achieve fine segmentation of high-resolution remote sensing images. Experimental results on three different segmentation datasets suggest that the proposed method has higher segmentation accuracy and better performance than other deep learning networks, especially for complicated shadow information. Code will be provided in (https://github.com/tingtingqu/code). |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 22797254 2279-7254 |
| Relation: | https://doaj.org/toc/2279-7254 |
| DOI: | 10.1080/22797254.2023.2174706 |
| URL الوصول: | https://doaj.org/article/e94bceb240294f26992215fd7627384c |
| رقم الأكسشن: | edsdoj.94bceb240294f26992215fd7627384c |
| قاعدة البيانات: | Directory of Open Access Journals |
PDF full text from Publisher 
Full Text Finder | تدمد: | 22797254 |
|---|---|
| DOI: | 10.1080/22797254.2023.2174706 |