High-Resolution Boundary Refined Convolutional Neural Network for Automatic Agricultural Greenhouses Extraction from GaoFen-2 Satellite Imageries
文献类型：期刊论文
| 作者 | Zhang, Xiaoping2,3; Cheng, Bo2,3; Chen, Jinfen2,3; Liang, Chenbin1,3
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| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2021-11-01 |
| 卷号 | 13期号:21页码:25 |
| 关键词 | Agricultural Greenhouses DCNN Semantic Segmentation high resolution context integration boundary refined GaoFen-2 |
| DOI | 10.3390/rs13214237 |
| 通讯作者 | Cheng, Bo(chengbo@aircas.ac.cn) |
| 英文摘要 | Agricultural greenhouses (AGs) are an important component of modern facility agriculture, and accurately mapping and dynamically monitoring their distribution are necessary for agricultural scientific management and planning. Semantic segmentation can be adopted for AG extraction from remote sensing images. However, the feature maps obtained by traditional deep convolutional neural network (DCNN)-based segmentation algorithms blur spatial details and insufficient attention is usually paid to contextual representation. Meanwhile, the maintenance of the original morphological characteristics, especially the boundaries, is still a challenge for precise identification of AGs. To alleviate these problems, this paper proposes a novel network called high-resolution boundary refined network (HBRNet). In this method, we design a new backbone with multiple paths based on HRNetV2 aiming to preserve high spatial resolution and improve feature extraction capability, in which the Pyramid Cross Channel Attention (PCCA) module is embedded to residual blocks to strengthen the interaction of multiscale information. Moreover, the Spatial Enhancement (SE) module is employed to integrate the contextual information of different scales. In addition, we introduce the Spatial Gradient Variation (SGV) unit in the Boundary Refined (BR) module to couple the segmentation task and boundary learning task, so that they can share latent high-level semantics and interact with each other, and combine this with the joint loss to refine the boundary. In our study, GaoFen-2 remote sensing images in Shouguang City, Shandong Province, China are selected to make the AG dataset. The experimental results show that HBRNet demonstrates a significant improvement in segmentation performance up to an IoU score of 94.89%, implying that this approach has advantages and potential for precise identification of AGs. |
| WOS关键词 | PLASTIC GREENHOUSES ; CLASSIFICATION |
| 资助项目 | National Natural Science Foundation of China[61731022] ; National Natural Science Foundation of China[61860206004] |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000719013600001 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/46521]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_影像分析与机器视觉团队 |
| 通讯作者 | Cheng, Bo |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China 3.Univ Chinese Acad Sci, Coll Resource & Environm, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Xiaoping,Cheng, Bo,Chen, Jinfen,et al. High-Resolution Boundary Refined Convolutional Neural Network for Automatic Agricultural Greenhouses Extraction from GaoFen-2 Satellite Imageries[J]. REMOTE SENSING,2021,13(21):25. |
| APA | Zhang, Xiaoping,Cheng, Bo,Chen, Jinfen,&Liang, Chenbin.(2021).High-Resolution Boundary Refined Convolutional Neural Network for Automatic Agricultural Greenhouses Extraction from GaoFen-2 Satellite Imageries.REMOTE SENSING,13(21),25. |
| MLA | Zhang, Xiaoping,et al."High-Resolution Boundary Refined Convolutional Neural Network for Automatic Agricultural Greenhouses Extraction from GaoFen-2 Satellite Imageries".REMOTE SENSING 13.21(2021):25. |
入库方式： OAI收割
来源：自动化研究所
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