Tree counting with high spatial-resolution satellite imagery based on deep neural networks
文献类型：期刊论文
| 作者 | Yao, Ling1,2,3; Liu, Tang2,4; Qin, Jun2,3,5; Lu, Ning1,2,3; Zhou, Chenghu1,2,3
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| 刊名 | ECOLOGICAL INDICATORS
 |
| 出版日期 | 2021-06-01 |
| 卷号 | 125页码:12 |
| 关键词 | Forestry Deep learning algorithms Remote sensing Density estimation Tree counting |
| ISSN号 | 1470-160X |
| DOI | 10.1016/j.ecolind.2021.107591 |
| 通讯作者 | Yao, Ling(yaoling@lreis.ac.cn) |
| 英文摘要 | Forest inventory at single-tree level is of great importance to modern forest management. The inventory contains two critical parameters about trees, including their numbers and spatial locations. Traditional methods to catalogue single trees are laborious, while deep neural networks enable to discover the multi-scale features hidden in images and thus make it possible to count trees with remote sensing imagery. In this study, four different tree counting networks, which were constructed by remodeling four different classical deep convolutional neural networks, were evaluated to determine their abilities to grasp the relationship between remote sensing images and tree locations for automatic tree counting end-to-end. To this end, a tree counting dataset was constructed with remote sensing images of 0.8-m spatial resolution in distinct regions. This dataset consisted of 24 GF-II images and the corresponding manually annotated locations of trees based on these images. Thereafter, a large number of experiments were conducted to examine the performance of these networks in regards to tree counting. The results demonstrated that all networks could achieve the competitive performance (above 0.91) in terms of the determination coefficient (R-2) between the ground truth and the estimated values. The average accuracy of the Encoder-Decoder Network (one of the four networks) was greater than 91.58% and its R-2 was equal to 0.97, achieving the best performance. It has been found that the deep learning is an efficient and effective means for tree counting task. |
| 资助项目 | National Natural Science Foundation of China[41771380] ; Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)[GML2019ZD0301] ; National Postdoctoral Program for Innovative Talents, China[BX20200100] ; National Data Sharing Infrastructure of Earth System Science |
| WOS研究方向 | Biodiversity & Conservation ; Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:000637798700004 |
| 出版者 | ELSEVIER |
| 资助机构 | National Natural Science Foundation of China ; Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) ; National Postdoctoral Program for Innovative Talents, China ; National Data Sharing Infrastructure of Earth System Science |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/161762]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Yao, Ling |
| 作者单位 | 1.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China 3.Southern Marine Sci & Engn Guangdong Lab, Guangzhou, Peoples R China 4.China Univ Geosci, Beijing, Peoples R China 5.Univ Chinese Acad Sci, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yao, Ling,Liu, Tang,Qin, Jun,et al. Tree counting with high spatial-resolution satellite imagery based on deep neural networks[J]. ECOLOGICAL INDICATORS,2021,125:12. |
| APA | Yao, Ling,Liu, Tang,Qin, Jun,Lu, Ning,&Zhou, Chenghu.(2021).Tree counting with high spatial-resolution satellite imagery based on deep neural networks.ECOLOGICAL INDICATORS,125,12. |
| MLA | Yao, Ling,et al."Tree counting with high spatial-resolution satellite imagery based on deep neural networks".ECOLOGICAL INDICATORS 125(2021):12. |
入库方式： OAI收割
来源：地理科学与资源研究所
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