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Enhancing DeepLabv3+Convolutional Neural Network Model for Precise Apple Orchard Identification Using GF-6 Remote Sensing Images and PIE-Engine Cloud Platform

文献类型：期刊论文

作者Gao, Guining1; Chen, Zhihan1; Wei, Yicheng1; Zhu, Xicun1; Yu, Xinyang1,2
刊名REMOTE SENSING
出版日期2025-05-31
卷号17期号:11页码:1923
关键词agricultural mapping apple orchard deep learning semantic segmentation remote sensing identification
DOI10.3390/rs17111923
产权排序2
文献子类Article
英文摘要Utilizing remote sensing models to monitor apple orchards facilitates the industrialization of agriculture and the sustainable development of rural land resources. This study enhanced the DeepLabv3+ model to achieve superior performance in apple orchard identification by incorporating ResNet, optimizing the algorithm, and adjusting hyperparameter configuration using the PIE-Engine cloud platform. GF-6 PMS images were used as the data source, and Qixia City was selected as the case study area for demonstration. The results indicate that the accuracies of apple orchard identification using the proposed DeepLabv3+_34, DeepLabv3+_50, and DeepLabv3+_101 reached 91.17%, 92.55%, and 94.37%, respectively. DeepLabv3+_101 demonstrated superior identification performance for apple orchards compared with ResU-Net and LinkNet, with an average accuracy improvement of over 3%. The identified area of apple orchards using the DeepLabv3+_101 model was 629.32 km2, accounting for 31.20% of Qixia City's total area; apple orchards were mainly located in the western part of the study area. The innovation of this research lies in combining image annotation and object-oriented methods during training, improving annotation efficiency and accuracy. Additionally, an enhanced DeepLabv3+ model was constructed based on GF-6 satellite images and the PIE-Engine cloud platform, exhibiting superior performance in feature expression compared with conventional machine learning classification and recognition algorithms.
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WOS关键词DEEP ; CLASSIFICATION
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001505847900001
出版者MDPI
源URL[http://ir.igsnrr.ac.cn/handle/311030/214638]  
专题中国科学院地理科学与资源研究所
通讯作者Yu, Xinyang
作者单位1.Shandong Agr Univ, Coll Resources & Environm, Tai An 271018, Peoples R China;
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Gao, Guining,Chen, Zhihan,Wei, Yicheng,et al. Enhancing DeepLabv3+Convolutional Neural Network Model for Precise Apple Orchard Identification Using GF-6 Remote Sensing Images and PIE-Engine Cloud Platform[J]. REMOTE SENSING,2025,17(11):1923.
APA Gao, Guining,Chen, Zhihan,Wei, Yicheng,Zhu, Xicun,&Yu, Xinyang.(2025).Enhancing DeepLabv3+Convolutional Neural Network Model for Precise Apple Orchard Identification Using GF-6 Remote Sensing Images and PIE-Engine Cloud Platform.REMOTE SENSING,17(11),1923.
MLA Gao, Guining,et al."Enhancing DeepLabv3+Convolutional Neural Network Model for Precise Apple Orchard Identification Using GF-6 Remote Sensing Images and PIE-Engine Cloud Platform".REMOTE SENSING 17.11(2025):1923.

入库方式： OAI收割

来源：地理科学与资源研究所

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