دورية أكاديمية
Semantic segmentation for plastic-covered greenhouses and plastic-mulched farmlands from VHR imagery
| العنوان: | Semantic segmentation for plastic-covered greenhouses and plastic-mulched farmlands from VHR imagery |
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| المؤلفون: | Bowen Niu, Quanlong Feng, Shuai Su, Zhi Yang, Sihang Zhang, Shaotong Liu, Jiudong Wang, Jianyu Yang, Jianhua Gong |
| المصدر: | International Journal of Digital Earth, Vol 16, Iss 2, Pp 4553-4572 (2023) |
| بيانات النشر: | Taylor & Francis Group, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Mathematical geography. Cartography |
| مصطلحات موضوعية: | Agricultural plastic covers, deep learning, semantic segmentation, satellite remote sensing, transfer learning, Mathematical geography. Cartography, GA1-1776 |
| الوصف: | ABSTRACTDue to their important role in maintaining temperature and soil moisture, agricultural plastic covers have been widely utilized around the globe for improving crop-growing conditions, which include both plastic-covered greenhouses (PCGs) and plastic-mulched farmlands (PMFs). However, it is a challenging and long-neglected issue to separate PCGs from PMFs due to their spectral similarity. The objective of this study is to propose a deep semantic segmentation model for accurate PCG and PMF mapping based on very high-resolution satellite images and to improve the model’s spatial generalization capability using a transfer learning strategy. Specifically, the proposed semantic segmentation model has an encoder-decoder structure, where the encoder is composed of a new convolutional neural network for discriminative spatial feature learning, while the decoder utilizes a multi-task strategy to improve the predictions on the boundaries. Meanwhile, a transfer learning framework is adopted to increase mapping performance and generalization ability under limited samples. Experimental results in several typical regions across the Eurasian continent show that the proposed model could separate PCGs from PMFs accurately with a mean overall accuracy of 94.49% and an average mIoU of 0.8377. Ablation studies verify the role of encoder-decoder and transfer learning strategy in improving classification performance. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 17538947 1753-8955 1753-8947 |
| Relation: | https://doaj.org/toc/1753-8947; https://doaj.org/toc/1753-8955 |
| DOI: | 10.1080/17538947.2023.2275657 |
| URL الوصول: | https://doaj.org/article/afe2d49448124963b013141a5cabafd0 |
| رقم الأكسشن: | edsdoj.fe2d49448124963b013141a5cabafd0 |
| قاعدة البيانات: | Directory of Open Access Journals |
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Full Text Finder | تدمد: | 17538947 17538955 |
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| DOI: | 10.1080/17538947.2023.2275657 |