Global perspectives on sand dune patterns: Scale-adaptable classification using Landsat imagery and deep learning strategies
文献类型：期刊论文
| 作者 | Zheng, Zhijia1,2; Zhang, Xiuyuan3; Li, Jiajun4; Ali, Eslam5; Yu, Jinsongdi1,2; Du, Shihong3 |
| 刊名 | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
 |
| 出版日期 | 2024-12-01 |
| 卷号 | 218页码:781-801 |
| 关键词 | Sand dune pattern Landform mapping Global Multiscale Deep learning |
| ISSN号 | 0924-2716 |
| DOI | 10.1016/j.isprsjprs.2024.10.002 |
| 英文摘要 | Sand dune patterns (SDPs) are spatial aggregations of dunes and interdunes, exhibiting distinct morphologies and spatial structures. Recognizing global SDPs is crucial for understanding the development processes, contributing factors, and self-organization characteristics of aeolian systems. However, the diversity, complexity, and multiscale nature of global SDPs poses significant technical challenges in the classification scheme, sample collection, feature representation, and classification method. This study addresses these challenges by developing a novel global SDP classification approach based on an advanced deep-learning network. Firstly, we established a globally applicable SDP classification scheme that accommodates the diversity nature of SDPs. Secondly, we developed an SDP semantic segmentation sample dataset, which encompassed a wide array of SDP representations. Thirdly, we deployed the SegFormer network to automatically capture detailed dune structures and developed a weighted voting strategy to ensure scale adaptability. Experiments utilizing Landsat-8 imagery yielded a commendable overall accuracy (OA) of 85.43 %. Notably, most SDP types exhibited high classification accuracies, such as star dunes (97.43 %) and simple linear dunes (87.17 %). The weighted voting strategy prioritized the predictions of each type, resulting in a 1.41 %similar to 7.91 % improvement in OA compared to the single-scale classification and average voting methods. This innovative approach facilitated the generation of a high-quality, fine-grained, and global-scale SDP map at 30 m resolution (GSDP30), which not only directly provides the spatial distribution of global SDPs but also serves as valuable support for understanding aeolian processes. This study represents the first instance of producing such a comprehensive and globally applicable SDP map at this fine resolution. |
| 资助项目 | National Natural Science Foundation of China[42401511] ; National Natural Science Foundation of China[42271469] ; Natural Science Foundation of Fujian Province, China[2023J05105] ; Ningbo Science and Technology Bureau[2022Z081] |
| WOS研究方向 | Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001339945200001 |
| 出版者 | ELSEVIER |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/39505]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Zhang, Xiuyuan |
| 作者单位 | 1.Fuzhou Univ, Natl & Local Joint Engn Res Ctr Satellite Geospati, Key Lab Spatial Data Min & Informat Sharing, Minist Educ, Fuzhou 350108, Peoples R China 2.Fuzhou Univ, Acad Digital China, Fuzhou 350108, Peoples R China 3.Peking Univ, Coll Urban & Environm Sci, Beijing 100871, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 5.Cairo Univ, Fac Engn, Publ Works Dept, Giza 12613, Egypt |
| 推荐引用方式 GB/T 7714 | Zheng, Zhijia,Zhang, Xiuyuan,Li, Jiajun,et al. Global perspectives on sand dune patterns: Scale-adaptable classification using Landsat imagery and deep learning strategies[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2024,218:781-801. |
| APA | Zheng, Zhijia,Zhang, Xiuyuan,Li, Jiajun,Ali, Eslam,Yu, Jinsongdi,&Du, Shihong.(2024).Global perspectives on sand dune patterns: Scale-adaptable classification using Landsat imagery and deep learning strategies.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,218,781-801. |
| MLA | Zheng, Zhijia,et al."Global perspectives on sand dune patterns: Scale-adaptable classification using Landsat imagery and deep learning strategies".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 218(2024):781-801. |
入库方式： OAI收割
来源：计算技术研究所
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