Query：
学者姓名：郑智嘉
Refining：
Year
Type
Indexed by
Source
Complex
Former Name
Co-
Language
Clean All
Abstract ：
地球剖分网格作为传统地理坐标参考的有效补充,具有形状近似、空间无缝不重叠、尺度连续、全球统一的刚性定位等优势,与REST(Representational State Transfer)Web服务的结合可完成空间区域位置标识下的影像数据互操作.基于此,本研究基于地球剖分网格探索遥感影像数据REST资源化方法,定义了面向资源架构(Re-source-Oriented Architecture,ROA)的URI标识模型,从面向资源的角度设计互操作协议函数,采用地球剖分网格单元的地址编码代替传统地理坐标以刚性定位资源化影像.最后,以北斗网格作为参考网格,开发分析就绪(A-nalysis-Ready)资源化影像服务原型系统,并开展互操作案例研究,验证了影像数据资源刚性定位互操作模型用于影像资源共享互操作的可行性和有效性.
Keyword ：
互操作 互操作 刚性定位 刚性定位 协议函数 协议函数 地球剖分网格 地球剖分网格 资源化 资源化
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 刘甫 , 余劲松弟 , 吴升 et al. 资源化影像刚性定位互操作模型研究 [J]. | 测绘与空间地理信息 , 2025 , 48 (2) : 7-12 . |
| MLA | 刘甫 et al. "资源化影像刚性定位互操作模型研究" . | 测绘与空间地理信息 48 . 2 (2025) : 7-12 . |
| APA | 刘甫 , 余劲松弟 , 吴升 , 邵远征 , 郑智嘉 , 佟瑞菊 et al. 资源化影像刚性定位互操作模型研究 . | 测绘与空间地理信息 , 2025 , 48 (2) , 7-12 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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.
Keyword ：
Deep learning Deep learning Global Global Landform mapping Landform mapping Multiscale Multiscale Sand dune pattern Sand dune pattern
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| 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 . |
| APA | Zheng, Zhijia , Zhang, Xiuyuan , Li, Jiajun , Ali, Eslam , Yu, Jinsongdi , Du, Shihong . Global perspectives on sand dune patterns: Scale-adaptable classification using Landsat imagery and deep learning strategies . | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING , 2024 , 218 , 781-801 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
