Mapping of Intrusive Complex on a Small Scale Using Multi-Source Remote Sensing Images

被引:1
|
作者
Zhang, Yuzhou [1 ]
Zhang, Dengrong [2 ]
Duan, Jinwei [2 ]
Hu, Tangao [2 ]
机构
[1] Zhejiang Univ, Sch Earth Sci, Hangzhou 310027, Peoples R China
[2] Hangzhou Normal Univ, Zhejiang Prov Key Lab Urban Wetlands & Reg Change, Hangzhou 311121, Peoples R China
关键词
intrusive complex; multi-source remote sensing; GF2; Sentinel-2; ASTER; synergy; SPACEBORNE THERMAL EMISSION; REFLECTION RADIOMETER ASTER; DATA PRODUCTS; AREA; ROCKS; DISCRIMINATION; INFORMATION; FUSION; MINERALIZATION; BELT;
D O I
10.3390/ijgi9090543
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Multi-stage intrusive complex mapping plays an important role in regional mineralization research. The similarity of lithology characteristics between different stages of intrusions necessitates the use of richer spectral bands, while higher spatial resolution is also essential in small-scale research. In this paper, a multi-source remote sensing data application method was proposed. This method includes a spectral synergy process based on statistical regression and a fusion process using Gram-Schmidt (GS) spectral sharpening. We applied the method with Gaofen-2 (GF2), Sentinel-2, and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data to the mapping of the Mountain Sanfeng intrusive complex in northwest China in which Carboniferous intrusions have been proven to be directly related to the formation of Au deposits in the area. The band ratio (BR) and relative absorption band depth (RBD) were employed to enhance the spectral differences between two stage intrusions, and the Red-Green-Blue (RGB) false colour of the BR and RBD enhancement images performed well in the west and centre. Excellent enhancement results were obtained by making full use of all bands of the synergistic image and using the Band Ratio Matrix (BRM)-Principal Component Analysis (PCA) method in the northeast part of the study area. A crucial improvement in enhancement performance by the GS fusion process and spectral synergy process was thus shown. An accurate mapping result was obtained at the Mountain Sanfeng intrusive complex. This method could support small-scale regional geological survey and mineralization research in this region.
引用
收藏
页数:22
相关论文
共 50 条
  • [41] Identifying Grassland Distribution in a Mountainous Region in Southwest China Using Multi-Source Remote Sensing Images
    Yuan, Yixin
    Wen, Qingke
    Zhao, Xiaoli
    Liu, Shuo
    Zhu, Kunpeng
    Hu, Bo
    REMOTE SENSING, 2022, 14 (06)
  • [42] A NOVEL WETLAND CLASSIFICATION METHOD COMBINED CNN AND SVM USING MULTI-SOURCE REMOTE SENSING IMAGES
    Cao, Jingmiao
    Shu, Feiya
    Xu, Hanwen
    Wu, Qinxin
    Niu, Yufen
    Zhao, Jinqi
    IGARSS 2024-2024 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, IGARSS 2024, 2024, : 4793 - 4796
  • [43] Wetland mapping in the Liaohe River Estuary using multi-source remote sensing image feature selection
    He, Jinjie
    Wang, Chang
    Han, Ying
    Zhang, Wen
    Wang, Xu
    Li, Yuxiang
    Guo, Li
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2024, 45 (18) : 6624 - 6650
  • [44] Multi-decadal Dutch coastal dynamic mapping with multi-source remote sensing imagery
    Zhang, Bin
    Chang, Ling
    Wang, Zhengbing
    Wang, Li
    Ye, Qinghua
    Stein, Alfred
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2025, 138
  • [45] A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images
    Fang, Chengyong
    Fan, Xuanmei
    Wang, Xin
    Nava, Lorenzo
    Zhong, Hao
    Dong, Xiujun
    Qi, Jixiao
    Catani, Filippo
    EARTH SYSTEM SCIENCE DATA, 2024, 16 (10) : 4817 - 4842
  • [46] Application of Artificial Neural Networks for Mangrove Mapping Using Multi-Temporal and Multi-Source Remote Sensing Imagery
    Ghorbanian, Arsalan
    Ahmadi, Seyed Ali
    Amani, Meisam
    Mohammadzadeh, Ali
    Jamali, Sadegh
    WATER, 2022, 14 (02)
  • [47] Research on multi-source remote sensing image registration technology based on Baker mapping
    Ma, Li
    Huang, Lei
    INTERNATIONAL JOURNAL OF IMAGE AND DATA FUSION, 2024, 15 (03) : 293 - 309
  • [48] A FRAMEWORK OF COLLABORATIVE CHANGE DETECTION WITH MULTIPLE OPERATORS AND MULTI-SOURCE REMOTE SENSING IMAGES
    Chen, Xi
    Li, Jing
    Zhang, Yunfei
    Tao, Liangliang
    Shen, Wei
    2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2016, : 5169 - 5172
  • [49] A matching method combining SIFT and edge information for multi-source remote sensing images
    Ye, Yuanxin
    Shan, Jie
    Xiong, Jinxin
    Dong, Laigen
    Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University, 2013, 38 (10): : 1148 - 1151
  • [50] Invariant Features Based Ship Detection Model for Multi-source Remote Sensing Images
    Yang X.
    Zhang X.
    Guo H.-Y.
    Wang N.-N.
    Gao X.-B.
    Tien Tzu Hsueh Pao/Acta Electronica Sinica, 2022, 50 (04): : 887 - 899