入河排污口遥感排查进展评述

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。

Review on the Progress of Remote Sensing Investigation of the Outfalls into Rivers

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.