Deposit Location Identification Based on Feature Decomposition of High-Resolution Remote Sensing Images

IEEE Access

Shulan Tang¹  Jiannong Cao² 

[1] School of Earth Science and Resources, Chang&x2019;an University, Xi&x2019;

关键词: High-resolution remote sensing image;    feature extraction;    mineral location;    target detection;   

DOI  :  10.1109/ACCESS.2020.3022626

来源: DOAJ

【 摘 要 】

In modern mineral exploration applications, remote sensing technologies have been widely used and affirmed by engineering and mineral industries due to their unique technical advantages. With the advancement of remote sensing technologies, multiple geological remote sensing-derived prospecting methods have been developed. With the development of more accurate sensors, the detection band has not been segmented, and the spectral resolution of such sensors is constantly being improved. Thus, the accuracy of remote sensing geological prospecting methods has improved, and geological prospecting results have shifted from being qualitative to quantitative in nature. In this article, high-resolution remote sensing images are used to extract the ore controlling factors of deposits. The color, shape, texture and other image shapes produced by high-resolution remote sensing images are fully exploited to comprehensively mine the available data utilizing mathematics, image processing methods and other technologies to systematically identify prospective target areas. Based on an analysis of the metalloorganic geological characteristics detected in the study area, combined with multisource data such as geophysical and geochemical exploration-derived observations, the proposed remote sensing model describing the deposits in the study area is summarized. The research results show that deposit location identification technologies based on high-resolution remote sensing image feature decomposition have the potential to provide a reliable basis for peripheral exploration and deposit positioning in geological and mineral exploration studies.

【 授权许可】

Unknown