Global perspectives on sand dune patterns: Scale-adaptable classification using Landsat imagery and deep learning strategies

doi:10.1016/j.isprsjprs.2024.10.002

	Global perspectives on sand dune patterns: Scale-adaptable classification using Landsat imagery and deep learning strategies
	Zheng, Zhijia 1,2; Zhang, Xiuyuan 3; Li, Jiajun 4; Ali, Eslam 5; Yu, Jinsongdi 1,2; Du, Shihong 3
	2024-12-01
发表期刊	ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
ISSN	0924-2716
卷号	218 页码:781-801
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.
关键词	Sand dune pattern Landform mapping Global Multiscale Deep learning
DOI	10.1016/j.isprsjprs.2024.10.002
收录类别	SCI
语种	英语
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
WOS类目	Geography, Physical ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001339945200001
出版者	ELSEVIER
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/39506
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.