Automatic recognition method of urban underground silt based on remote sensing image—a case of Anqing City
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摘要:
沟塘河渠可能由于各种原因被填埋而形成暗浜，对城市的工程建设造成质量隐患。相比于传统的探测暗浜的方法，如物探、微动探测技术，遥感监测具有监测范围广、效率高、可重复观测等优势。利用遥感图像变化检测方法提取安庆市城区暗浜空间位置与范围，基于面向对象的图像分析方法，分别对多时相影像进行先分割，进而利用SVM算法进行监督分类，得到研究区的多时相影像土地覆盖分类结果。基于2期图像分类结果，进行变化检测分析，提取暗浜的空间分布与范围，并选择典型区域利用微动探测进行实地验证。提出的城区暗浜提取方法能够为城市工程建设与城市规划提供决策支持，并且为实施物探划定出靶区或重点区域，提高物探工作效率。
Abstract:Underground silt, due to complex and loose compound, is a potential threat in urban infrastructure.Compared with the traditional methods of detecting underground silt, such as geophysical and micro-motion detection technology, remote sensing monitoring has the advantages of wide monitoring range, high efficiency and repeatability. The detection method of remote sensing image change was used to extract the spatial location and area of underground silt in urban area of Anqing. The method was mainly based on the object-oriented image analysis method, first splitting the multi-temporal images separately, and then using the SVM algorithm to classify the land cover. Based on the classification results, the spatial distribution of underground silt was extracted by change detection analysis, which could be defined as the target area or key area for the implementation of physical exploration, so as to detect the depth of the underground silt. Based on the results of two phases of image classification, change detection analysis was carried out to extract the spatial distribution and range of underground silt, and select typical areas for field verification using microtremor detection. The proposed method can provide decision support for urban engineering construction and urban planning. It can delineate the target area or key area for geophysical exploration, and improve the efficiency of geophysical exploration.
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致谢: 感谢安徽省地球物理地球化学勘查技术院提供的微动探测技术支持，感谢审稿专家对本文的评论及建议。
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图 1 研究区概况及遥感影像数据
a—研究区位置图；b—T2(2018年)影像；c—T1(2004年)影像
Figure 1. Illustration of the study area and remote sensing images
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图 4 微动测量工作布置示意图(2004影像)
a—测线1和测线2；b—测线3
Figure 4. Layout of microtremor survey (images of 2004)
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图 5 影像分类结果
a—研究区位置图；b—T2影像分类结果；c—T1影像水体提取结果
Figure 5. The classification results of remote sensing images
表 1 分类精度
Table 1 Accuracy of classification
影像类型 总体精度 Kappa系数 T2影像 90.49% 0.8506 T1影像 96.12% 0.94 
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表 2 暗浜空间范围分布面积
Table 2 Area statistics of underground silt
km2 名称 建设用地 耕地 林地 草地 未利用土地 暗浜总计 宜秀区 2.3368 0.0486 0.0053 0.1844 — 2.5751 十里铺 2.0097 0.0064 0.0510 0.0008 — 2.0679 老峰镇 1.6146 0.3560 0.0010 0.0050 0.0093 1.9859 白泽湖 0.5841 0.0622 — — — 0.6463 山口乡 0.5852 — 0.0186 0.0010 — 0.6048 长风乡 0.5028 0.0607 0.0016 0.0067 — 0.5718 龙狮乡 0.5120 0.0496 — — — 0.5616 大龙山 0.3972 0.0021 0.0055 0.0159 — 0.4207 大观区 0.0932 — 0.0007 0.0000 — 0.0939 月山镇 0.0716 — 0.0072 0.0035 0.0007 0.0830 海口镇 0.0003 0.0659 0.0000 0.0000 — 0.0662 迎江区 0.0184 0.0268 0.0045 0.0138 — 0.0635 菱北街 0.0306 — — 0.0010 — 0.0316 杨桥镇 0.0049 — 0.0180 0.0024 — 0.0253 鲟鱼镇 0.0225 — 0.0009 0.0000 — 0.0234 五横乡 — — 0.0202 0.0015 — 0.0217 新洲乡 0.0090 0.0091 — 0.0018 — 0.0199 总计 8.7929 0.6874 0.1345 0.2378 0.0100 9.8626
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