Chen, Y.; Wang, Y.; Zhang, F.; Dong, Y.; Song, Z.; Liu, G. Remote Sensing for Lithology Mapping in Vegetation-Covered Regions: Methods, Challenges, and Opportunities. Minerals2023, 13, 1153.
Chen, Y.; Wang, Y.; Zhang, F.; Dong, Y.; Song, Z.; Liu, G. Remote Sensing for Lithology Mapping in Vegetation-Covered Regions: Methods, Challenges, and Opportunities. Minerals 2023, 13, 1153.
Chen, Y.; Wang, Y.; Zhang, F.; Dong, Y.; Song, Z.; Liu, G. Remote Sensing for Lithology Mapping in Vegetation-Covered Regions: Methods, Challenges, and Opportunities. Minerals2023, 13, 1153.
Chen, Y.; Wang, Y.; Zhang, F.; Dong, Y.; Song, Z.; Liu, G. Remote Sensing for Lithology Mapping in Vegetation-Covered Regions: Methods, Challenges, and Opportunities. Minerals 2023, 13, 1153.
Abstract
Remote sensing (RS) technology has significantly contributed to geological exploration and mineral resource assessment. However, its effective application in vegetated areas encounters various challenges. This paper aims to provide a comprehensive overview of the challenges and opportunities associated with RS-based lithological identification in vegetated regions. The article begins by introducing the sources and processing methods of RS data, which serve as the foundation for subsequent analysis. Moreover, it highlights the techniques and methodologies employed for lithological classification in vegetated areas. Notably, hyperspectral RS and Synthetic Aperture Radar (SAR) have emerged as prominent tools in lithological identification. In addition, this paper addresses the limitations inherent in RS technology, including issues related to vegetation cover and terrain effects, which significantly impact accurate lithological mapping. To propel further advancements in the field, the paper proposes promising avenues for future research and development. These include the integration of multi-source data to enhance classification accuracy and the exploration of novel RS technologies and algorithms. In summary, this paper presents valuable insights and recommendations for advancing the study of RS-based lithological identification in vegetated areas.
Keywords
Lithology Mapping; Machine Learning; Deep Learning; Feature Extraction; Remote Sensing; Vegetated area
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
