Mountain Vegetation Classification Method Based on Multi-Channel Semantic Segmentation Model
文献类型：期刊论文
| 作者 | Wang, Baoguo1,2; Yao, Yonghui1 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2024 |
| 卷号 | 16期号:2页码:16 |
| 关键词 | vegetation classification multi-source image remote sensing semantic segmentation multi-channel model deep learning |
| DOI | 10.3390/rs16020256 |
| 通讯作者 | Yao, Yonghui(yaoyh@lreis.ac.cn) |
| 英文摘要 | With the development of satellite remote sensing technology, a substantial quantity of remote sensing data can be obtained every day, but the ability to extract information from these data remains poor, especially regarding intelligent extraction models for vegetation information in mountainous areas. Because the features of remote sensing images (such as spectral, textural and geometric features) change with changes in illumination, viewing angle, scale and spectrum, it is difficult for a remote sensing intelligent interpretation model with a single data source as input to meet the requirements of engineering or large-scale vegetation information extraction and updating. The effective use multi-source, multi-resolution and multi-type data for remote sensing classification is still a challenge. The objective of this study is to develop a highly intelligent and generalizable classification model of mountain vegetation utilizing multi-source remote sensing data to achieve accurate vegetation extraction. Therefore, a multi-channel semantic segmentation model based on deep learning, FCN-ResNet, is proposed to integrate the features and textures of multi-source, multi-resolution and multi-temporal remote sensing data, thereby enhancing the differentiation of different mountain vegetation types by capturing their characteristics and dynamic changes. In addition, several sets of ablation experiments are designed to investigate the effectiveness of the model. The method is validated on Mt. Taibai (part of the Qinling-Daba Mountains), and the pixel accuracy (PA) of vegetation classification reaches 85.8%. The results show that the proposed multi-channel semantic segmentation model can effectively discriminate different vegetation types and has good intelligence and generalization ability in different mountainous areas with similar vegetation distributions. The multi-channel semantic segmentation model can be used for the rapid updating of vegetation type maps in mountainous areas. |
| 资助项目 | National Key RD Program |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:001151503500001 |
| 资助机构 | National Key RD Program |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/202375]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Yao, Yonghui |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Baoguo,Yao, Yonghui. Mountain Vegetation Classification Method Based on Multi-Channel Semantic Segmentation Model[J]. REMOTE SENSING,2024,16(2):16. |
| APA | Wang, Baoguo,&Yao, Yonghui.(2024).Mountain Vegetation Classification Method Based on Multi-Channel Semantic Segmentation Model.REMOTE SENSING,16(2),16. |
| MLA | Wang, Baoguo,et al."Mountain Vegetation Classification Method Based on Multi-Channel Semantic Segmentation Model".REMOTE SENSING 16.2(2024):16. |
入库方式： OAI收割
来源：地理科学与资源研究所
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