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Remote Sensing for Natural Resources    2023, Vol. 35 Issue (2) : 25-33     DOI: 10.6046/zrzyyg.2022141
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
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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.

Keywords opencast mining      mining area features      extraction method based on remote sensing images      object-based image analysis      deep learning     
ZTFLH:  TP79  
Issue Date: 07 July 2023
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Wei LI
Zhaoying YANG
Baocheng DOU
Haomin CHEN
Cite this article:   
Xian ZHANG,Wei LI,Li CHEN, et al. Research progress and prospect of remote sensing-based feature extraction of opencast mining areas[J]. Remote Sensing for Natural Resources, 2023, 35(2): 25-33.
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序号 一级类别 二级类别
1 采矿区 露天采场、集水坑等
2 中转场地 矿石堆、选矿场、洗矿场等
3 尾矿区 排土场、废石堆、尾矿库等
4 矿山建筑物 选矿厂、冶炼厂等
5 地质灾害 采矿沉陷、地裂缝、崩塌、滑坡、泥石流等
6 矿山环境 道路、植被、水体、裸土等
Tab.1  Categories of elements in opencast mining areas
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