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Rapid lithological mapping using multi-source remote sensing data fusion and automatic sample generation strategy

文献类型：期刊论文

作者Zhang, Tao5,6,7; Zhao, Zhifang1,6,7,8; Dong, Pinliang2; Tang, Bo-Hui3,4,10; Zhang, Geng5; Feng, Lunxin6; Zhang, Xinle9
刊名INTERNATIONAL JOURNAL OF DIGITAL EARTH
出版日期2024-12-31
卷号17期号:1页码:23
关键词Lithological mapping sample auto-generation data fusion Google Earth Engine object-based segmentation
ISSN号1753-8947
DOI10.1080/17538947.2024.2420824
产权排序9
英文摘要The advancement of remote sensing technology aids geologists in obtaining lithological maps more quickly, comprehensively, and accurately. However, key challenges in lithological mapping include the limited spectral information from individual sensors and the difficulties in visually interpreting lithological samples. In this study, we integrated 241 scenes of optical data and 106 scenes of radar data on the Google Earth Engine (GEE) platform, proposing a rapid lithological identification framework that combines an automatic lithological sample data generation strategy with multi-source data. Using various machine learning algorithms, we evaluated the classification capabilities of heterogeneous predictive factors, feature optimization algorithms, and object-based algorithms. Results indicate that: (1) Combining optical and radar data improves prediction accuracy, with terrain data further enhancing mapping capabilities; (2) Terrain factors contribute most to classification, but SWIR and TIR bands of optical data are critical for lithological identification; (3) The feature optimization algorithm reduces feature redundancy and efficiency issues from multi-source data, achieving 96.51% accuracy with the optimal feature model, an improvement of 0.1%-2.02% over original features; (4) Object-based algorithms show significant potential in mapping areas with large rock outcrops. This study offers new insights for medium- to large-scale lithological maps and provides essential data support for geological work.
WOS关键词IMAGERY ; ASTER ; RICE ; MAP
资助项目National Natural Science Foundation of China[4216106]
WOS研究方向Physical Geography ; Remote Sensing
语种英语
WOS记录号WOS:001349228200001
出版者TAYLOR & FRANCIS LTD
资助机构National Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/211066]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Zhao, Zhifang
作者单位1.Engn Res Ctr Domest High Resolut Satellite Remote, Kunming, Peoples R China
2.Univ North Texas, Dept Geog & Environm, Denton, TX USA
3.Yunnan Prov Dept Educ, Key Lab Plateau Remote Sensing, Kunming, Peoples R China
4.Kunming Univ Sci & Technol, Fac Land Resource Engn, Kunming, Peoples R China
5.Yunnan Univ, Inst Int Rivers & Ecosecur, Kunming, Peoples R China
6.Yunnan Int Joint Lab China Laos Bangladesh Myanmar, Kunming, Peoples R China
7.Yunnan Key Lab Sanjiang Metallogeny & Resources Ex, Kunming, Peoples R China
8.Yunnan Univ, Sch Earth Sci, Kunming, Peoples R China
9.Geol Sci Res Inst Yunnan Prov, Kunming, Peoples R China
10.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Tao,Zhao, Zhifang,Dong, Pinliang,et al. Rapid lithological mapping using multi-source remote sensing data fusion and automatic sample generation strategy[J]. INTERNATIONAL JOURNAL OF DIGITAL EARTH,2024,17(1):23.
APA Zhang, Tao.,Zhao, Zhifang.,Dong, Pinliang.,Tang, Bo-Hui.,Zhang, Geng.,...&Zhang, Xinle.(2024).Rapid lithological mapping using multi-source remote sensing data fusion and automatic sample generation strategy.INTERNATIONAL JOURNAL OF DIGITAL EARTH,17(1),23.
MLA Zhang, Tao,et al."Rapid lithological mapping using multi-source remote sensing data fusion and automatic sample generation strategy".INTERNATIONAL JOURNAL OF DIGITAL EARTH 17.1(2024):23.

入库方式： OAI收割

来源：地理科学与资源研究所

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