“算法-标志”协同的小微黑臭水体高分遥感识别方法  

A High-Resolution Remote Sensing Identification Method for Small,Black and Odorous Water Bodies Coordinated by"Algorithm-Flag"

作　　者：孙永彬[1,2,3] 卢辉雄 李启亮[1,3] 张恩 汪冰 刘硕[4] 牛海威 王少帅[1,3] 薛庆 欧琪琪 SUN Yongbin;LU Huixiong;LI Qiliang;ZHANG En;WANG Bing;LIU Shuo;NIU Haiwei;WANG Shaoshuai;XUE Qing;OU Qiqi(Airborne Survey and Remote Sensing Center of Nuclear Industry,Shijiazhuang 050002,China;Key Laboratory of Airborne Survey and Remote Sensing,Shijiazhuang 050002,China;High-resolution Earth Observation System Data Application Technical Support Center of Hebei Province,Shijiazhuang 050002,China;Hebei Geological Environment Monitoring Institute,Shijiazhuang 050004,China)

机构地区：[1]核工业航测遥感中心,石家庄050002 [2]河北省航空探测与遥感技术重点实验室,石家庄050002 [3]高分辨率对地观测系统河北数据应用技术支持中心,石家庄050002 [4]河北省地质环境监测院,石家庄050004

出　　处：《地球信息科学学报》2024年第12期2788-2804,共17页Journal of Geo-information Science

基　　金：2023年度河北省平原区农村疑似黑臭水体遥感调查与动态监测项目(2023066);核工业航测遥感中心自主科研项目(2024-18)。

摘　　要：识别城市黑臭水体对于水环境整治、城市水生态保护具有重要作用,小微黑臭水体因其规模小、分散性强、流动性差、污染源复杂等特点采用传统遥感技术难以识别。本文为提高小微黑臭水体识别精度,提出一种基于高分影像的综合遥感“识别算法-识别标志”协同识别技术。以河北省保定市14个建成区小微黑臭水体为实验区,选取2023年春季、夏季、秋季的GF-2遥感影像数据,利用波段比值法构建城市小微黑臭水体遥感识别模型,结合黑臭水体形成机理及成因分析,在GF-2影像上建立水体颜色、形状、纹理、次生环境、沟渠淤塞、岸线垃圾等遥感识别标志,综合识别算法和识别标志得到最终识别结果,利用“目测+无人机航拍+水质检测”的方式进行了精度验证。结果表明:(1)通过精度验证分析,黑臭水体点位识别精确率(P1)为85.29%、灵敏度(P2)为90.63%、准确率(P3)为94.74%,黑臭水体面积识别精度(P4)为91.19%,该方法可有效识别城市小微黑臭水体;(2)对比波段比值法和遥感识别标志所占权重可知,识别算法和水体颜色权重分别占比25.38%和21.11%,二者对城市小微黑臭水体识别起主要判识作用;(3)遥感识别错误的小微黑臭水体占比17.1%,识别遗漏的小微黑臭水体占比8.57%,错分率、漏分率相对较低;(4)对比春季、夏季、秋季同一水体特征表明,综合遥感识别技术能够较好地反映黑臭水体时空变化特征。在精度指标中,本研究的“算法-标志”方法较红绿波段比值法、差值法、WCI指数法等,点位识别精度至少提升1.88%,面积识别精度至少提升1.95%,说明该方法具有更好的识别精度,可为其它城市黑臭水体“长制久清”提供技术支撑。The identification of urban black and odorous water bodies plays an important role in water environment regulation and urban water ecological protection.Small black and odorous water bodies are difficult to identify using traditional remote sensing technology due to their small scale,high dispersion,poor mobility,and complex pollution sources.To improve the recognition accuracy of these water bodies,this paper presents an integrated remote sensing method based on high-resolution imagery,combining a"recognition algorithm"with“recognition marks”.Using GF2 remote sensing image data from spring,summer,and autumn of 2023,a remote sensing model for identifying small urban black and odorous water bodies was developed through the band ratio method,alongside an analysis of the formation mechanisms and causes of these water bodies.Remote sensing markers such as water color,shape,texture,secondary environment,ditch blockage,and shoreline garbage were established on the GF2 images.The final identification result was achieved by integrating the recognition algorithm and identification markers,with accuracy verified through"visual inspection+UAV aerial photography+water quality testing".The results show that:(1)Through precision verification analysis reveals that the accuracy rate(P1),sensitivity(P2),accuracy(P3),and area identification accuracy(P4)of black and odorous water bodies are 85.29%,90.63%,94.74%,and 91.19%,respectively,demonstrating the method’s effectiveness in identifying small and slightly black and odorous water bodies in urban areas;(2)By comparing the weights of the band ratio method and remote sensing identification markers,it was found that the recognition algorithm and water color weight accounted for 25.38%and 21.11%,respectively,playing a significant role in identifying small,dark,and odorous urban water bodies;(3)The proportion of small black and odorous water bodies incorrectly identified by remote sensing is 17.1%,while the proportion of missed detections is 8.57%,indicating relatively low miscla

关 键 词：城市建成区 小微黑臭水体 GF-2 识别算法 识别标志 点位识别精度 面积识别精度 

分 类 号：X832[环境科学与工程—环境工程] X87