基于时序InSAR的珠江三角洲形变区自动识别与监测

doi:10.13474/j.cnki.11-2246.2024.0915

测绘通报 ›› 2024, Vol. 0 ›› Issue (9): 80-86,95.doi: 10.13474/j.cnki.11-2246.2024.0915

基于时序InSAR的珠江三角洲形变区自动识别与监测

吴培洪¹, 钟少忠², 罗澍然³, 谢荣安¹

1. 广东省地质测绘院, 广东广州 510800;
2. 广东省测绘产品质量监督检验中心, 广东广州 510075;
3. 中南大学地球科学与信息物理学院, 湖南长沙 410083

收稿日期:2024-01-10 发布日期:2024-10-09
通讯作者: 钟少忠。E-mail:251373837@qq.com
作者简介:吴培洪(1981—),男,高级工程师,主要从事测绘地理信息及管理工作。E-mail:274677745@qq.com

Automatic identification and monitoring of deformation areas in the Pearl River Delta based on time-series InSAR

WU Peihong¹, ZHONG Shaozhong², LUO Shuran³, XIE Rongan¹

1. Guangdong Geological Surveying and Mapping Institute, Guangzhou 510800, China;
2. Guangdong Provincial Surveying and Mapping Product Quality Supervision and Inspection Center, Guangzhou 510075, China;
3. School of Earth Science and Information Physics, Central South University, Changsha 410083, China

Received:2024-01-10 Published:2024-10-09

摘要/Abstract

摘要： 合成孔径雷达干涉测量(InSAR)技术已被广泛应用于地表形变监测。然而当监测范围较广、地表形变区域较多时,目视解译形变区工作量大且易误判、漏判。基于此,本文提出了一种InSAR形变区自动识别算法,用于地表形变区域识别与监测。以地质灾害点较多的珠江三角洲为研究区域,首先利用InSAR技术获取该地区2015年6月至2020年11月的地表形变;然后利用所提算法自动圈定形变区域,共识别出630个疑似地质灾害隐患点;最后结合实地调查和历史光学影像等信息,对部分重点监测区域内形变的时空发育特征和产生原因等进行了分析。本文研究应用于珠三角地面沉降的识别和监测,极大地减少解译工作强度,提高解译精度及工作效率,为大范围InSAR地表形变监测及珠三角地区形变特征分析和地面沉降地质灾害风险防控提供技术支撑。

关键词: 珠江三角洲, 地面沉降, InSAR, 形变区识别

Abstract: The interferometric synthetic aperture radar (InSAR) technology has been widely applied in surface deformation monitoring. However, when the monitoring range is wide and there are many surface deformation areas, visual interpretation of deformation areas requires a large amount of work and is prone to misjudgment and omission. Based on this, this article proposes an InSAR deformation area automatic recognition algorithm for surface deformation area recognition and monitoring. In this paper, the the Pearl River Delta, which has many geological hazards, is taken as the research area. Firstly, InSAR technology is used to obtain the surface deformation of this area from June 2015 to November 2020. Then, using the proposed algorithm, the deformation area is automatically delineated, and a total of 630 suspected geological hazard points are identified. Finally, based on field investigations and historical optical images, the spatio-temporal development characteristics and causes of deformation in some key monitoring areas are analyzed.The application of the research results in this article to the identification and monitoring of ground subsidence in the Pearl River Delta has a good demonstration effect, which can greatly reduce the intensity of interpretation work, improve interpretation accuracy and efficiency, and provide technical support for large-scale InSAR surface deformation monitoring, analysis of deformation characteristics in the Pearl River Delta region, and risk prevention and control of ground subsidence geological hazards.

Key words: The Pearl River Delta, land subsidence, InSAR, deformation area identification

中图分类号:

P237

吴培洪, 钟少忠, 罗澍然, 谢荣安. 基于时序InSAR的珠江三角洲形变区自动识别与监测[J]. 测绘通报, 2024, 0(9): 80-86,95.

WU Peihong, ZHONG Shaozhong, LUO Shuran, XIE Rongan. Automatic identification and monitoring of deformation areas in the Pearl River Delta based on time-series InSAR[J]. Bulletin of Surveying and Mapping, 2024, 0(9): 80-86,95.

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基于时序InSAR的珠江三角洲形变区自动识别与监测

Automatic identification and monitoring of deformation areas in the Pearl River Delta based on time-series InSAR

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