Academic Journal
Water extraction from optical high-resolution remote sensing imagery: a multi-scale feature extraction network with contrastive learning
| العنوان: | Water extraction from optical high-resolution remote sensing imagery: a multi-scale feature extraction network with contrastive learning |
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| المؤلفون: | Bo Liu, Shihong Du, Lubin Bai, Song Ouyang, Haoyu Wang, Xiuyuan Zhang |
| المصدر: | GIScience & Remote Sensing, Vol 60, Iss 1 (2023) |
| بيانات النشر: | Taylor & Francis Group, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Mathematical geography. Cartography LCC:Environmental sciences |
| مصطلحات موضوعية: | water extraction, high-resolution remote sensing images, multi-scale features, contrastive learning, Mathematical geography. Cartography, GA1-1776, Environmental sciences, GE1-350 |
| الوصف: | Accurately spatiotemporal distribution of water bodies is of great importance in the fields of ecology and environment. Recently, convolutional neural networks (CNN) have been widely used for this purpose due to their powerful features extraction ability. However, the CNN methods have two limitations in extracting water bodies. First, the large variations in both the spatial and spectral characteristics of water bodies require that the CNN-based methods have the ability of extracting multi-scale features and using multi-layer features. Second, collecting enough samples is a difficult problem in the training phase of CNN. Therefore, this paper proposed a multi-scale features extraction network (MSFENet) for water extraction, and its advantages are contributed to two distinct features: (1) scale features extractor (MSFE) is designed to extract multi-layer multi-scale features of water bodies; (2) contrastive learning (CL) is adopted to reduce the sample size requirement. Experimental results show that MSFE can effectively improve the small water body extraction performance, and the CL can significantly improve the extraction accuracy when the training sample size is small. Compared with other methods, MSFENet achieves the highest F1-score and kappa coefficient in two datasets. Furthermore, spectral variability analysis shows that MSFENet is more robust than other neural networks in a spectrum variation scenario. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1548-1603 1943-7226 15481603 |
| Relation: | https://doaj.org/toc/1548-1603; https://doaj.org/toc/1943-7226 |
| DOI: | 10.1080/15481603.2023.2166396 |
| URL الوصول: | https://doaj.org/article/e6a7d232a1ba4673adf29db8dd80ceb9 |
| رقم الانضمام: | edsdoj.6a7d232a1ba4673adf29db8dd80ceb9 |
| قاعدة البيانات: | Directory of Open Access Journals |
| تدمد: | 15481603 19437226 |
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| DOI: | 10.1080/15481603.2023.2166396 |