摘要:
露天开采矿区要素遥感提取是矿业活动监测研究中的热门话题,但少有对相关研究的系统梳理和总结。为此,该文首先对露天开采矿区要素进行了界定,按要素种类将要素提取分为单要素提取和多要素提取,并简述了与一般地物提取和土地利用分类的区别; 其次,简要总结了目前相关研究的遥感数据来源与处理平台; 然后,将露天开采矿区要素遥感提取方法分为目视解译方法、基于传统特征的方法和深度学习方法3类,分别总结其研究现状,并分析了各方法的优缺点以及适用情况; 最后,对露天开采矿区要素遥感提取的未来研究方向进行了展望。文章认为有效地利用多源多时相数据、更强特征提取能力网络和复杂场景优化方法,进一步推动矿区要素智能化、精细化和鲁棒性提取是未来发展的趋势。研究结果可为露天开采矿区要素遥感提取的研究与应用提供参考。
Abstract:
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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