WO2022213673A1 - 融合无人机dom和星载sar影像的地表三维形变提取方法 - Google Patents

融合无人机dom和星载sar影像的地表三维形变提取方法 Download PDF

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WO2022213673A1
WO2022213673A1 PCT/CN2021/141462 CN2021141462W WO2022213673A1 WO 2022213673 A1 WO2022213673 A1 WO 2022213673A1 CN 2021141462 W CN2021141462 W CN 2021141462W WO 2022213673 A1 WO2022213673 A1 WO 2022213673A1
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deformation
image
dom
horizontal movement
sar
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PCT/CN2021/141462
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French (fr)
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范洪冬
庄会富
谭志祥
张宏贞
郝明
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中国矿业大学
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B7/00—Measuring arrangements characterised by the use of electric or magnetic techniques
    • G01B7/16—Measuring arrangements characterised by the use of electric or magnetic techniques for measuring the deformation in a solid, e.g. by resistance strain gauge
    • G01B7/24—Measuring arrangements characterised by the use of electric or magnetic techniques for measuring the deformation in a solid, e.g. by resistance strain gauge using change in magnetic properties
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/86—Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
    • G01S13/867—Combination of radar systems with cameras
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
    • G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
    • G01S13/9004—SAR image acquisition techniques
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position

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  • the invention relates to a method for obtaining three-dimensional deformation of the surface by fusing unmanned aerial vehicle DOM and spaceborne SAR images, and belongs to the field of surface deformation and disaster monitoring.
  • the present invention combines the advantages of UAV images and SAR images, and proposes a three-dimensional surface deformation extraction method that integrates UAV DOM and spaceborne SAR images, which can quickly and accurately obtain the three-dimensional deformation of the surface and structures. bright future.
  • the technical problem to be solved by the present invention is to provide a method for extracting three-dimensional deformation of the ground surface that integrates UAV DOM and spaceborne SAR images, which solves the problem that it is difficult to obtain three-dimensional surface deformation from single-track SAR images and that it is difficult to obtain vertical sinking from UAV images. It has the advantages of high precision, low cost, no contact with the measured object, wide range, and easy operation.
  • the method for extracting three-dimensional surface deformation of the fusion UAV DOM and spaceborne SAR images of the present invention is characterized in that the specific steps are as follows:
  • S1 use satellite SAR/InSAR technology to calculate the line-of-sight deformation field of the target area, denoted as: LOS;
  • S3 take the first phase DOM1 as the main image and the second phase DOM2 as the slave image, use the fine registration method to calculate the pixel offsets in the north-south and east-west directions of the two DOM image points with the same name, and remove from the offset the two
  • the overall offset of the image obtained by the secondary drone is the pixel offset caused by the movement of the ground; the overall offset of the image is the error offset generated by the two aerial photography;
  • the surface three-dimensional deformation extraction method of fusion UAV DOM and spaceborne SAR image according to claim 1 is characterized in that, what SAR/InSAR technology adopts in step S1 is classical offset tracking algorithm, subband Interferometric method, DInSAR, and time-series InSAR, the above-mentioned methods can be used to obtain the line-of-sight deformation of the surface, which is recorded as LOS.
  • the surface three-dimensional deformation extraction method of fusion UAV DOM and spaceborne SAR image according to claim 1 is characterized in that, the two-phase DOM ground resolution and SAR image resolution generated in step S2 should be identical, otherwise The DOM needs to be resampled.
  • the precise registration method in step S3 includes: a normalized cross-correlation matching method, a least squares matching method, and a feature matching method; the overall offset of the image is obtained from the quadratic surface fitted by the offset of the non-deformed area, which mainly includes: It is the systematic error caused by the registration algorithm and the influence of noise.
  • step S4 the horizontal movement amount U N in the north-south direction and the east-west horizontal movement amount U E of each pixel with the same name are calculated, and the unit of the movement amount is the number of pixels, specifically:
  • step S5 the formula for calculating the vertical surface settlement W by using the SAR three-dimensional deformation decomposition model is:
  • ⁇ is the incident angle of the radar satellite
  • ⁇ h is the heading angle of the satellite
  • U N and U E are the horizontal movement in the north-south and east-west directions calculated by the DOM of the UAV
  • LOS is the surface deformation of the radar line of sight obtained by SAR/InSAR technology.
  • Single-track SAR technology can only obtain high-precision radar line-of-sight (LOS) deformation, and cannot be decomposed into three-dimensional deformation in vertical, east-west, and north-south directions.
  • the difference between two phases of UAV image formation of DEM can only obtain low-precision vertical deformation.
  • horizontal movement is also lack of research and application.
  • the invention combines the advantages of the UAV image and the SAR image, uses the UAV image accurate registration method to obtain the horizontal movement, and brings it into the SAR line-of-sight deformation decomposition equation, so that the LOS deformation can be decomposed to obtain high-precision vertical deformation.
  • FIG. 1 is a flowchart of the implementation of the method for extracting three-dimensional deformation of the ground surface by fusing the UAV DOM and spaceborne SAR images according to the present invention.
  • FIG. 2 is a three-dimensional deformation diagram of the simulated ground surface used in the present invention.
  • FIG. 3 is a three-dimensional deformation map of the surface calculated by the present invention.
  • the method for extracting the three-dimensional deformation of the surface of the fusion UAV DOM and spaceborne SAR images of the present invention is characterized in that the specific steps are as follows:
  • S1 use satellite SAR/InSAR technology to solve the line-of-sight deformation field of the target area, denoted as: LOS;
  • SAR/InSAR technology uses the classic offset tracking algorithm, sub-band interference method, DInSAR, time-series InSAR, which can be used The above method obtains the surface line-of-sight deformation variable, denoted as LOS.
  • S3 take the first phase of DOM1 as the main image and the second phase of DOM2 as the slave image, and use the precise registration method to calculate the pixel offsets in the north-south and east-west directions of the two DOM image points with the same name.
  • the precise registration method includes: normalization Cross-correlation matching method, least squares matching method, feature matching method; the overall offset of the image is obtained from the quadratic surface fitted by the offset of the non-deformed area, which is mainly caused by the registration algorithm and the systematic error caused by the influence of noise; In this offset, the pixel offset caused by the ground surface movement is obtained by removing the overall offset of the images obtained by the two drones; the overall image offset is the error offset generated by the two aerial photography, and the error offset The amount is the overall pixel offset caused by the registration method, noise, etc. These offsets are used to fit the entire offset of the area, and the entire image removes these offsets to leave the true offset of the deformed area.
  • the three-dimensional deformation un, ue, and w of the surface of a simulated mine are calculated through the mining subsidence prediction model and simulation parameters; the resolution of the simulated SAR image is 0.221m; the LOS data is simulated according to the SAR three-dimensional deformation decomposition model.
  • the DOM is resampled according to the simulated three-dimensional deformation value of the ground surface, and the new DOM is used as the DOM generated by the UAV image at time t 2 .
  • a method for extracting three-dimensional deformation of the ground surface by fusing unmanned aerial vehicle DOM and spaceborne SAR images comprising the following steps, specifically:
  • the interval between the two flights of the UAV should be consistent with the time interval of the acquired SAR images, the altitude and camera parameters used in the two flights should be consistent, and the upper left corner of the DOM generated after processing should be the same, and the DOM
  • the ground resolution should be the same as the SAR image resolution. After resampling, the ground resolution of the obtained DOM is 0.221m, and the size of the deformation study area is 1185 ⁇ 823 pixels.
  • the normalized cross-correlation matching method For the two-phase DOM, the normalized cross-correlation matching method, the feature matching method, the least square image matching and the same name point matching method are used to realize the rough registration and fine registration of the DOM
  • the vertical deformation value W of each point on the surface is calculated.
  • the formula is:
  • ⁇ is the incident angle of the radar satellite
  • ⁇ h is the heading angle of the satellite
  • U N and U E are the horizontal movement in the north-south and east-west directions calculated by the DOM registration of the UAV
  • LOS is the radar line-of-sight obtained by SAR/InSAR technology to the surface deformation.

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  • Engineering & Computer Science (AREA)
  • Remote Sensing (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Electromagnetism (AREA)
  • Radar Systems Or Details Thereof (AREA)
  • Image Processing (AREA)
  • Details Of Aerials (AREA)
  • Radio Relay Systems (AREA)

Abstract

一种融合无人机DOM和星载SAR影像的地表三维形变提取方法，适用于地表形变及地质灾害监测领域。该方法包括：利用SAR或InSAR技术获取目标区地表LOS向形变；利用无人机获取目标区地表影像数据，生成具有相同分辨率的数字正射影像图DOM；利用精配准方法计算两期DOM上同名像素点在东西向、南北向的坐标偏移量，结合DOM分辨率，求得各像素点对应地表点的东西和南北方向的水平移动；将利用DOM获取的东西、南北向水平移动及LOS向形变代入SAR三维形变模型，解算地表竖向下沉值W，从而得到地表三维形变。该方法结合无人机DOM和星载SAR影像获取地表三维形变，覆盖范围广、非接触地表、效果好，为地表三维形变监测提供了一种新方法。

Description

融合无人机DOM和星载SAR影像的地表三维形变提取方法 技术领域
本发明涉及一种融合无人机DOM和星载SAR影像获取地表三维形变的方法，属于地表形变及灾害监测领域。
技术背景
我国幅员辽阔，自然环境多样，每年因地下资源开发导致的地表沉降、地质灾害量大、面广。比如：城市地区地表及地下工程建设、地下水资源开发导致地表沉降，进而影响地表建构筑物安全运营；煤炭、石油、金属等矿产资源开发后，会对矿区环境产生严重损害，形成地表塌陷、裂缝、滑坡等灾害；我国西部地区，尤其是云南、贵州、四川、青海等省份，地形起伏大，各类地质灾害频发。这些地表形变、地质灾害本质上是各地表点移动轨迹的综合反映，可以在三维空间中进行投影，分解为竖向移动和水平移动。竖向移动为下沉或隆起，水平移动可以按照垂直或平行于某一断面进行设置，比如：南北向及东西向水平移动。
传统地表形变监测方法如：GNSS、水准测量、全站仪等存在工作量大、点位密度不足、点位易破坏、成本高、不便于进行连续测量和自动化测量等缺点。合成孔径雷达(SAR)测量技术自上世纪90年代发展以来，有效弥补了传统监测技术的不足。目前SAR已广泛应用于区域灾害探测、监测领域。然而，由于该技术只能获取沿雷达视线方向地表形变，单轨道SAR影像在不借助外部数据或数学模型的情况下难以获取地表三维形变，极大地限制了其在建构筑物形变及地质灾害监测方面的应用。随着无人机技术的成熟与普及，无人机摄影测量已广泛应用于各行各业，其优势在于机动灵活、非接触被摄物体、分辨率高、速度快、精度高，但该技术难以获取地表竖直沉降，也缺乏直接将其用于地表水平移动获取的广泛应用。
为此，本发明结合无人机影像及SAR影像的优势，提出一种融合无人机DOM和星载SAR影像的地表三维形变提取方法，可以快速、高精度获取地表及建构筑物三维形变，应用前景广阔。
发明内容
本发明所要解决的技术问题是：提供一种融合无人机DOM和星载SAR影像的地表三维形变提取方法，解决了单轨SAR影像难以获取地表三维形变、无 人机影像难以得到竖向下沉的问题，具有精度高、成本低、不接触被测物体、范围广、易操作等优点。
为实现上述技术目的，本发明的融合无人机DOM和星载SAR影像的地表三维形变提取方法，其特征在于具体步骤如下：
S1，利用卫星SAR/InSAR技术解算目标地区的视线向形变场，记为：LOS；
S2，利用一架无人机按照一样的航路获取两个不同时期的目标地区地表影像数据，处理无人机影像生成数字正射影像图DOM，且两期DOM空间分辨率相同；
S3，将第一期DOM1作为主影像，第二期DOM2作为从影像，利用精配准方法计算两期DOM同名像点南北、东西方向的像素偏移量，从该偏移量中去除因为两次无人机获得的影像整体偏移量得到因地表移动而引起的像素偏移量；影像整体偏移量为两次航拍产生的误差偏移量；
S4，利用S3得到的南北、东西方向的像素偏移量和影像地面分辨率计算每个同名像点相应地表实际水平移动，每个同名像点实际水平移动包括：南北方向水平移动量U N、东西水平移动量U E；
S5，根据SAR三维形变分解模型，结合卫星获取的视线向形变场LOS以及南北方向水平移动量U N、东西水平移动量U E，解算目标区域地表竖向下沉值W，从而得到地表实际三维形变。
2.根据权利要求1所述的融合无人机DOM和星载SAR影像的地表三维形变提取方法，其特征在于，步骤S1中SAR/InSAR技术采用的是经典的偏移量跟踪算法、子带干涉方法、DInSAR、时序InSAR，可利用上述方法获取地表视线向形变量，记为LOS。
3.根据权利要求1所述的融合无人机DOM和星载SAR影像的地表三维形变提取方法，其特征在于，步骤S2中生成的两期DOM地面分辨率与SAR影像分辨率应相同，否则需要对DOM进行重采样。
步骤S3中的精配准方法包括：归一化互相关匹配方法、最小二乘匹配方法、特征匹配方法；影像整体偏移量由非变形区域偏移量拟合的二次曲面获取，其主要是配准算法、噪声影响产生的系统误差。
步骤S4中计算各同名像素点的南北方向水平移动量U N、东西水平移动量 U E，移动量的单位为像素点个数，具体为：
设第一期DOM1与第二期DOM2匹配到的同名点对为p 1(x 1,y 1)和p 2(x 2,y 2)，点p 1(x 1,y 1)位于第一期DOM1上，点p 2(x 2,y 2)位于第二期DOM2上，(x 1,y 1)、(x 2,y 2)分别是点p 1(x 1,y 1)、点p 2(x 2,y 2)在各自影像坐标系中的坐标，影像坐标系的原点为DOM的左上角，原点向右的方向为影像坐标系X轴方向，原点向下的方向为影像坐标系Y轴方向；利用公式U N(x,y)＝GSD*(y 2-y 1)和U E(x,y)＝GSD*(x 2-x 1)分别计算第一期DOM1与第二期DOM2中记录的地表水平移动；U N(x,y)是同名点对p 1(x 1,y 1)和p 2(x 2,y 2)在南北方向上的水平移动，U E(x,y)是同名点对p 1(x 1,y 1)和p 2(x 2,y 2)在东西方向上的水平移动，GSD为DOM的地面分辨率。
步骤S5中利用SAR三维形变分解模型解算地表竖直沉降W的公式为：
Figure PCTCN2021141462-appb-000001
其中，θ为雷达卫星入射角；α h为卫星航向角；U N、U E为无人机DOM计算得到的南北、东西方向水平移动；LOS为SAR/InSAR技术得到的雷达视线向地表形变。
有益效果
单轨道SAR技术只能得到高精度雷达视线向(LOS)形变，不能分解到竖直、东西及南北方向的三维形变，无人机影像形成两期DEM做差只能得到低精度竖直向变形，水平移动也缺乏研究和应用。本发明融合了无人机影像及SAR影像的优势，利用无人机影像精确配准方法得到水平移动，带入SAR视线向变形分解方程，便可将LOS形变分解获取高精度的竖直向变形，弥补了两者各自难以获取高精度地表三维形变的不足，有效获取了地表及建构筑物的三维形变，破解了单轨道SAR影像只能获取视线向形变的难题，也拓展了无人机摄影测量的应用领域，具有精度高、成本低、不接触被测物体、范围广、易操作等优点，为地表及建构筑物三维形变信息提取、地质灾害监测与预警提供了一种新的技术手段。
附图说明
图1为本发明融合无人机DOM和星载SAR影像的地表三维形变提取方法的实施流程图。
图2为本发明使用的模拟地表三维形变图。
图3为本发明解算出的地表三维形变图。
具体实施方式
以下将结合具体实施过程对本发明做进一步说明，
如图1所示，本发明的融合无人机DOM和星载SAR影像的地表三维形变提取方法，其特征在于具体步骤如下：
S1，利用卫星SAR/InSAR技术解算目标地区的视线向形变场，记为：LOS；SAR/InSAR技术采用的是经典的偏移量跟踪算法、子带干涉方法、DInSAR、时序InSAR，可利用上述方法获取地表视线向形变量，记为LOS。
S2，利用一架无人机按照一样的航路获取两个不同时期的目标地区地表影像数据，生成的两期DOM地面分辨率与SAR影像分辨率应相同，否则需要对DOM进行重采样；处理无人机影像生成数字正射影像图DOM，且两期DOM空间分辨率相同；
S3，将第一期DOM1作为主影像，第二期DOM2作为从影像，利用精配准方法计算两期DOM同名像点南北、东西方向的像素偏移量，精配准方法包括：归一化互相关匹配方法、最小二乘匹配方法、特征匹配方法；影像整体偏移量由非变形区域偏移量拟合的二次曲面获取，其主要是配准算法、噪声影响产生的系统误差；从该偏移量中去除因为两次无人机获得的影像整体偏移量得到因地表移动而引起的像素偏移量；影像整体偏移量为两次航拍产生的误差偏移量，误差偏移量为配准方法、噪声等引起的整体像元偏移，用这些偏移拟合出区域的整个偏移，整幅影像去除这些偏移留下变形区域的真正偏移量。
S4，利用S3得到的南北、东西方向的像素偏移量和影像地面分辨率计算每个同名像点相应地表实际水平移动，每个同名像点实际水平移动包括：南北方向水平移动量U N、东西水平移动量U E；
S5，根据SAR三维形变分解模型，结合卫星获取的视线向形变场LOS以及南北方向水平移动量U N、东西水平移动量U E，解算目标区域地表竖向下沉值W，从而得到地表实际三维形变。
实施例一、
以煤炭开采模拟数据获取地表三维形变为例。某模拟矿工作面的走向长度 D 1＝155m；倾向长度D 2＝110m；煤层的走向方位角
Figure PCTCN2021141462-appb-000002
煤层倾角α＝0°；平均开采深度为H＝300m；煤层开采厚度m＝4000mm；模拟SAR影像雷达卫星入射角为37.28°；卫星航向角为176.52°。通过开采沉陷预计模型和模拟参数计算某模拟矿地表三维形变un、ue、w；模拟SAR影像分辨率为0.221m；根据SAR三维形变分解模型模拟出LOS数据。以某矿区t 1时间无人机DOM为例，根据模拟的地表三维形变值对DOM进行重采样，将新的DOM作为t 2时间无人机影像生成的DOM。
一种融合无人机DOM和星载SAR影像的地表三维形变提取方法，包括以下步骤，具体的：
1)利用SAR/InSAR技术解算目标地区的视线向形变场LOS；
获取目标地区不同时期的SAR影像，利用经典的偏移量跟踪算法、子带干涉方法、DInSAR、时序InSAR等算法求得两时期间目标地区的视线向形变数据LOS。
2)利用无人机获取两期目标地区地表影像数据，生成数字正射影像图DOM；
无人机两次飞行间隔时期应与获取的SAR影像时间间隔一致，在两次飞行所使用的航高和相机参数应保持一致，同时保证处理之后生成的DOM的左上角坐标相同，并且DOM的地面分辨率应与SAR影像分辨率相同，经重采样处理，所得DOM的地面分辨率为0.221m，形变研究区域大小为1185×823像素。
3)利用精配准方法将两期DOM中的同名像素点进行匹配；
对两期DOM采用归一化互相关匹配方法、特征匹配方法、最小二乘影像匹配等同名点匹配方法实现对DOM的粗配准和精配准
4)计算各像素点相应地表实际水平移动U N、U E；
对于匹配到的同名点对p 1(x 1,y 1)和p 2(x 2,y 2)，点p 1(x 1,y 1)位于DOM1上，点p 2(x 2,y 2)位于DOM2上，(x 1,y 1)、(x 2,y 2)分别是点p 1(x 1,y 1)、点p 2(x 2,y 2)在各自影像坐标系中的坐标，影像坐标系的原点为DOM的左上角，原点向右的方向为影像坐标系X轴方向，原点向下的方向为影像坐标系Y轴方向；按照公式U N(x,y)＝22.1×(y 2-y 1)和U E(x,y)＝22.1×(x 2-x 1)计算两个时期间的地表水平移动，U N(x,y)是同名点对p 1(x 1,y 1)和p 2(x 2,y 2)在南北方向上的水平移动，U E(x,y)是同名点对p 1(x 1,y 1)和p 2(x 2,y 2)在东西方向 上的水平移动。
解算地表点竖向形变值W；
根据SAR三维形变分解模型，结合LOS值以及利用无人机DOM计算出的东西、南北方向的水平移动U N、U E，解算地表各点竖向形变值W，其公式为：
Figure PCTCN2021141462-appb-000003
其中，θ为雷达卫星入射角；α h为卫星航向角；U N、U E为无人机DOM配准计算得到的南北、东西方向水平移动；LOS为SAR/InSAR技术得到的雷达视线向地表形变。
5)U N、U E、W三个方向的解算结果如图3，与原始模拟地表三维形变un、ue、w如图2所示，之间的均方根误差分别为12.16mm、10.05mm、7.56mm，这是去除边缘匹配效果不好的40个像素点的计算结果。

Claims (6)

  1. 一种融合无人机DOM和星载SAR影像的地表三维形变提取方法，其特征在于具体步骤如下：
    S1，利用卫星SAR/InSAR技术解算目标地区的视线向形变场，记为：LOS；
    S2，利用一架无人机按照一样的航路获取两个不同时期的目标地区地表影像数据，处理无人机影像生成数字正射影像图DOM，且两期DOM空间分辨率相同；
    S3，将第一期DOM1作为主影像，第二期DOM2作为从影像，利用精配准方法计算两期DOM同名像点南北、东西方向的像素偏移量，从该偏移量中去除影像整体偏移量得到因地表移动而引起的像素偏移量；影像整体偏移量为两次航拍产生的误差偏移量；
    S4，利用S3得到的南北、东西方向的像素偏移量和影像地面分辨率计算每个同名像点相应地表实际水平移动，每个同名像点实际水平移动包括：南北方向水平移动量U N、东西水平移动量U E；
    S5，根据SAR三维形变分解模型，结合卫星获取的视线向形变场LOS以及南北方向水平移动量U N、东西水平移动量U E，解算目标区域地表竖向下沉值W，从而得到地表实际三维形变。
  2. 根据权利要求1所述的融合无人机DOM和星载SAR影像的地表三维形变提取方法，其特征在于：步骤S1中SAR/InSAR技术采用的是经典的偏移量跟踪算法、子带干涉方法、DInSAR、时序InSAR，可利用上述方法获取地表视线向形变量，记为LOS。
  3. 根据权利要求1所述的融合无人机DOM和星载SAR影像的地表三维形变提取方法，其特征在于：步骤S2中生成的两期DOM地面分辨率与SAR影像分辨率应相同，否则需要对DOM进行重采样。
  4. 根据权利要求1所述的融合无人机DOM和星载SAR影像的地表三维形变提取方法，其特征在于：步骤S3中的精配准方法包括：归一化互相关匹配方法、最小二乘匹配方法、特征匹配方法；影像整体偏移量由非变形区域偏移量拟合的二次曲面获取，其主要是配准算法、噪声影响产生的系统误差。
  5. 根据权利要求1所述的融合无人机DOM和星载SAR影像的地表三维形变提取方法，其特征在于：步骤S4中计算各同名像素点的南北方向水平移动量 U N、东西水平移动量U E，移动量的单位为像素点个数，具体为：
    设第一期DOM1与第二期DOM2匹配到的同名点对为p 1(x 1,y 1)和p 2(x 2,y 2)，点p 1(x 1,y 1)位于第一期DOM1上，点p 2(x 2,y 2)位于第二期DOM2上，(x 1,y 1)、(x 2,y 2)分别是点p 1(x 1,y 1)、点p 2(x 2,y 2)在各自影像坐标系中的坐标，影像坐标系的原点为DOM的左上角，原点向右的方向为影像坐标系X轴方向，原点向下的方向为影像坐标系Y轴方向；利用公式U N(x,y)＝GSD*(y 2-y 1)和U E(x,y)＝GSD*(x 2-x 1)分别计算第一期DOM1与第二期DOM2中记录的地表水平移动；U N(x,y)是同名点对p 1(x 1,y 1)和p 2(x 2,y 2)在南北方向上的水平移动，U E(x,y)是同名点对p 1(x 1,y 1)和p 2(x 2,y 2)在东西方向上的水平移动，GSD为DOM的地面分辨率。
  6. 根据权利要求5所述的融合无人机DOM和星载SAR影像的地表三维形变提取方法，其特征在于：步骤S5中利用SAR三维形变分解模型解算地表竖直沉降W的公式为：
    Figure PCTCN2021141462-appb-100001
    其中，θ为雷达卫星入射角；α h为卫星航向角；U N、U E为无人机DOM计算得到的南北、东西方向水平移动；LOS为SAR/InSAR技术得到的雷达视线向地表形变。
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