Research progress and prospect of remote sensing-based feature extraction of opencast mining areas
ZHANG Xian1,2(), LI Wei1,2, CHEN Li1, YANG Zhaoying1,2, DOU Baocheng3, LI Yu1(), CHEN Haomin1
1. China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China 2. Key Laboratory of Airborne Geophysics and Remote Sensing Geology, Ministry of Natural Resources, Beijing 100083, China 3. Zhijiang Lab, Beijing 100086, China
The remote sensing-based feature extraction of opencast mining areas is a hot topic in research on the monitoring of mining activities. However, there is a lack of systematic reviews and summaries of relevant studies. Therefore, this study first defined the features of an opencast mining area, divided the feature extraction into single- and multi-feature extractions according to feature types, and briefly described the differences between the feature extraction of opencast mining areas and general surface feature extraction and land use classification. Then, this study briefly summarized the sources and data processing platforms of remote sensing images available in relevant studies. Subsequently, this study divided the remote sensing-based methods for the feature extraction of opencast mining areas into three categories, namely visual interpretation, traditional feature-based approach, and deep learning. Then, it summarized the research status of these methods and analyzed their advantages, disadvantages, and applicability. Finally, this study proposed the future research direction of the remote sensing-based feature extraction of opencast mining areas, holding that the future developmental trend is to further promote the intelligent, fine-scale, and robust feature extraction of mining areas by effectively utilizing multi-source and multi-temporal data, networks with a stronger feature extraction capacity, and methods for the optimization of complex scenes. The results of this study can be used as a reference for the study and application of remote sensing-based feature extraction of opencast mining areas.
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2023, Vol. 35 
), 李伟1,2, 陈理1, 杨昭颖1,2, 窦宝成3, 李瑜1(
