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遥感技术与应用  2022, Vol. 37 Issue (1): 24-33    DOI: 10.11873/j.issn.1004-0323.2022.1.0024
青促会十周年专栏     
入河排污口遥感排查进展评述
黄耀欢1,2(),熊标1,2(),杨海军3,伍程斌1,2,朱海涛3
1.中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京 100101
2.中国科学院大学资源环境学院,北京 100049
3.生态环境部卫星环境应用中心,北京 100094
Review on the Progress of Remote Sensing Investigation of the Outfalls into Rivers
Yaohuan Huang1,2(),Biao Xiong1,2(),Haijun Yang3,Chengbin Wu1,2,Haitao Zhu3
1.State Key Laboratory of Resources and Environmental Information System,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China
2.College of Resource and Environment,University of Chinese Academy of Sciences,Beijing 100049,China
3.Ministry of Ecology and Environment Center for Satellite Application on Ecology and Environment,Beijing 100094,China
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摘要:

入河排污口是人为污染物流入河流的最后一道关卡,对其进行精确排查在水资源保护、水污染防治等工作中具有重要作用。首先回顾了近30 a来国内大型入河排污口排查工作情况,分别从人工实地调查、GIS台账系统建设、卫星遥感监测和无人机排查4个方面进行介绍;其次,在分析了直接目视解译、基于水环境参数反演以及基于地物分类等常用入河排污口遥感监测技术的基础之上,讨论了上述方法在无人机影像上应用的局限性;再次,通过简要介绍深度学习目标检测方法原理,评述了基于深度学习的目标检测方法在入河排污口无人机遥感排查上的应用现状及其关键技术;最后,对深度学习在无人机影像入河排污口识别上的应用前景进行了分析,并对今后包括入河排污口在内的复杂地理要素的无人机遥感监测的研究重点进行了展望。

关键词: 入河排污口排查遥感无人机目标检测    
Abstract:

Outfalls into rivers are the last checkpoint for man-made pollutants flowing into rivers. Accurate investigation of them plays an important role in the protection of water resources and the prevention and control of water pollution. Firstly, the progress of large-scale investigations of outfalls into rivers in the past 30 years were reviewed and the four aspects of manual field survey, GIS accounting system construction, satellite remote sensing monitoring and Unmanned Aerial Vehicle (UAV) investigation are introduced. Secondly, after analyzing remote sensing monitoring techniques for outfalls into rivers, which are based on direct visual interpretation, water environment parameters inversion and ground targets classification and other methods commonly used, the limitations of the application of the above methods on UAV images are discussed. And then, through introducing briefly the principle of the object detection method based on deep learning, the application status and key techniques of the deep learning-based object detection method implemented on the UAV remote sensing investigation of outfalls into rivers are discussed. Finally, analyzing the application prospect of deep learning on the recognition of outfalls into rivers using UAV imagery and looking forward the research emphasis of monitoring complex geographical objects including outfalls into rivers based on UAV remote sensing technique.

Key words: Outfalls into river    Investigation    Remote sensing    Unmanned Aerial Vehicle (UAV)    Object detection
收稿日期: 2021-10-20 出版日期: 2022-04-08
ZTFLH:  X522  
基金资助: 国家重点研发计划(2021YFC3002103);中国科学院A类战略性先导科技专项(XDA19040402);国家自然科学基金项目(41971359);高分共性产品真实性检验关键技术研究与标准规范编制(21?Y20B01?9001?19/22)
通讯作者: 熊标     E-mail: huangyh@igsnrr.ac.cn;xiongbiao3691@igsnrr.ac.cn
作者简介: 黄耀欢(1982-),男,安徽黄山人,副研究员,主要从事无人机遥感应用研究。E?mail: huangyh@igsnrr.ac.cn
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引用本文:

黄耀欢,熊标,杨海军,伍程斌,朱海涛. 入河排污口遥感排查进展评述[J]. 遥感技术与应用, 2022, 37(1): 24-33.

Yaohuan Huang,Biao Xiong,Haijun Yang,Chengbin Wu,Haitao Zhu. Review on the Progress of Remote Sensing Investigation of the Outfalls into Rivers. Remote Sensing Technology and Application, 2022, 37(1): 24-33.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2022.1.0024        http://www.rsta.ac.cn/CN/Y2022/V37/I1/24

图1  中国近30 a来入河排污口排查工作发展情况
传感器数据源空间分辨率/m时间分辨率/d波段组成幅宽/km应用方法
光学QuickBird0.65/2.61-3.55个波段,包括3个可见光波段、1个近红外波段和1个全色波段18直接检测[13]
光学GF-20.81/3.241-55个波段,包括3个可见光波段、1个近红外波段和1个全色波段45直接检测[25]
光学Landsat-5 TM30/120167个波段,包括3个可见光波段、1个近红外波段、2个短波红外波段和1个热红外波段185间接检测[13]
光学Landsat-8 OLI30/15169个波段,包括1个海岸气溶胶波段、3个可见光波段、1个近红外波段、3个短波红外波段和1个全色波段185间接检测[20]
光学Landsat-8 TIRS100162个波段,包括2个热红外波段185间接检测[20]
光学Terra ASTER15/30/901615个波段,包括3个可见光波段、1个近红外波段、6个短波红外波段和5个热红外波段60间接检测[22]
光学Aqua MODIS250/500/1 0001-236个波段,其中21个波段位于0.4~3.0 μm,15个波段位于3~14.5 μm2 300间接检测[20]
光学SeaWiFS1 000/4 00018个波段,1~6波段带宽20 nm,7~8波段带宽40 nm1 500/2 800间接检测[18]
雷达ERS-1/23035SAR图像100间接检测[21]
雷达Radarsat-130/10024SAR图像100/500间接检测[21]
雷达Enfisat-1 ASAR3030SAR图像100间接检测[22]
表1  入河排污口监测常用遥感数据源
图2  基于深度学习的目标检测算法分类及其发展历史
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