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。
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.
作者
张仙
李伟
陈理
杨昭颖
窦宝成
李瑜
陈昊旻
ZHANG Xian;LI Wei;CHEN Li;YANG Zhaoying;DOU Baocheng;LI Yu;CHEN Haomin(China Aero Geophysical Survey and Remote Sensing Center for Natural Resources,Beijing 100083,China;Key Laboratory of Airborne Geophysics and Remote Sensing Geology,Ministry of Natural Resources,Beijing 100083,China;Zhijiang Lab,Beijing 100086,China)
出处
《自然资源遥感》
CSCD
北大核心
2023年第2期25-33,共9页
Remote Sensing for Natural Resources
基金
自然资源部航空物理与遥感地质重点实验室课题“面向对象的红土型镍矿多要素识别提取与矿业活动变化定量分析研究”(编号:2020YFL32)和“基于深度学习的滑坡体识别方法研究”(编号:2020YFL26)共同资助。
关键词
露天开采
矿区要素
遥感提取方法
面向对象影像分析
深度学习
opencast mining
mining area features
extraction method based on remote sensing images
object-based image analysis
deep learning
