Journal
JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS
Volume 29, Issue 6, Pages -Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0218126620300068
Keywords
High-spatial-resolution images; geology interpreting; deep learning; remote sensing
Funding
- National Natural Science Foundation of China [U1711266, 41701516]
- Fundamental Research Funds for Central Universities, China University of Geosciences (Wuhan) [CUG170648]
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Geologists employ high-spatial-resolution (HR) remote sensing (RS) data for many diverse applications as they effectively reflect detailed geological information, enabling high-quality and efficient geological surveys. Applications of HR RS data to geological and related fields have grown recently. HR optical remote sensing data are widely used in geological hazard assessment, seismic monitoring, mineral exploitation, glacier monitoring, and mineral information extraction due to high accuracy and clear object features. By reviewing these applications, we can better understand the results of previous studies and more effectively use the latest data and methods to efficiently extract key geological information. Compared with optical satellite images, synthetic-aperture radar (SAR) images are stereoscopic and exhibit clear relief, strong performance, and good detection of terrain, landforms, and other information. SAR images have been applied to seismic mechanism research, volcanic monitoring, topographic deformation, and fault analysis. Furthermore, a multi-standard maturity analysis of the geological applications of HR images reveals that optical remote sensing data are superior to radar data for mining, geological disaster, lithologic, and volcanic applications, but inferior for earthquake, glacial, and fault applications. Therefore, it is necessary for geological remote sensing research to be truly multi-disciplinary or inter-disciplinary, ensuring more detailed and efficient surveys through cross-linking with other disciplines. Moreover, the recent application of deep learning technology to remote sensing data extraction has improved the capabilities of automatic processing and data analysis with HR images.
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