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CN111854699A - Unmanned aerial vehicle-based monitoring method for aerial survey river channel bank collapse process - Google Patents

Unmanned aerial vehicle-based monitoring method for aerial survey river channel bank collapse process Download PDF

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Publication number
CN111854699A
CN111854699A CN202010635673.XA CN202010635673A CN111854699A CN 111854699 A CN111854699 A CN 111854699A CN 202010635673 A CN202010635673 A CN 202010635673A CN 111854699 A CN111854699 A CN 111854699A
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bank
aerial vehicle
unmanned aerial
river
data
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夏军强
李志威
邓珊珊
周美蓉
刘鑫
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Wuhan University WHU
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Wuhan University WHU
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C11/00Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
    • G01C11/04Interpretation of pictures
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C7/00Tracing profiles
    • G01C7/02Tracing profiles of land surfaces
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02ATECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/30Assessment of water resources

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  • General Physics & Mathematics (AREA)
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Abstract

The invention discloses a monitoring method for a river bank collapse process based on aerial survey of an unmanned aerial vehicle, which belongs to the field of topographic data processing and comprises the following steps: carrying out unmanned aerial vehicle low-altitude aerial survey in the selected river reach, and determining the flight height and the overlapping degree of the UAV according to the requirements on the spatial resolution of different river bank parameters to obtain aerial survey image data; correcting the terrain data by adopting an RTK carrier phase difference technology; DEM denoising is carried out by using an image processing tool to obtain high-precision and high-resolution topographic data, the length a, the width b, the bank slope width c and the bank slope height h of a bank collapse in a river reach are obtained, and the soil volume and the critical gradient of critical balance are calculated; and calculating the topographic change of the bank collapse river reach by using ArcMap through multiple times of aerial survey data, and further calculating the annual bank collapse volume of the river reach scale. The invention can save manpower, simplify the working process and efficiently monitor the river bank collapse process.

Description

Unmanned aerial vehicle-based monitoring method for aerial survey river channel bank collapse process
Technical Field
The invention belongs to the field of topographic data processing, relates to interdisciplines such as riverbed evolution science, sediment motion mechanics, unmanned aerial vehicle aerial survey technology application and the like, and particularly relates to a monitoring method based on an unmanned aerial vehicle aerial survey riverway bank collapse process.
Background
The bank collapse is a direct result of river channel impact water flow to the bank erosion, is essentially a centralized embodiment of an accumulative process of slope toe erosion and a bank slope collapse and mutation process caused by the fact that the bank soil body reaches a static balance state, and has important influence on the transverse migration and the wriggling and creeping movement of a bent river. Types of bank collapse are various, and can be divided into washing collapse, strip collapse and pit collapse according to plane forms. According to the mechanical mode, the collapse can be divided into shallow layer collapse, plane sliding, arc sliding and cantilever type collapse. The bank collapse can cause the rapid expansion of the bank and the transportation of a large amount of silt, and can greatly change the water and sand transportation condition of a local river section in a short time, thereby causing severe influence on the evolution of a riverbed.
The existing bank collapse monitoring mainly comprises the steps of observing water sand and topographic data before and after flood to obtain related bank collapse information (such as river bank height, slope, toe scouring amplitude and the like); measuring underwater and land terrains by using electronic instruments such as a total station, an electronic level, a GNSS, a single-beam sounding system, a multi-beam sounding system and the like; and extracting information of the landform and the landform of the monitored river reach based on the remote sensing image, and reflecting the change process of the shoreline. In addition, the measurement of the flow, the sand content and the water level change of each hydrological and water level section along the way can be used for analyzing and researching the water level fluctuation and the flood peak process of the river reach in real time. By controlling the sailing ADCP on the spot, the local water flow condition of the bank collapse section can be obtained. With the development and application of technologies such as automatic monitoring equipment, network information communication, computers and the like, equipment such as a GNSS, a multipoint displacement meter, a land pressure meter and the like is adopted in a targeted manner to realize real-time monitoring, data such as near-shore riverbed deformation and riverbed boundary change of a dangerous work section are collected and transmitted, and the development trend of the riverbed of the dangerous work section is analyzed. However, due to the limitation of field conditions, the installation, maintenance and detection point arrangement of these detection devices have great difficulty, so that the practical bank collapse observation and monitoring is relatively rarely applied at present.
The existing technical means and measuring method need to consume a large amount of manpower and carry heavy tools, are complex to operate and low in precision, and bring inevitable errors due to manual operation.
Disclosure of Invention
Aiming at the defects or the improvement requirements in the prior art, the invention provides a monitoring method based on the unmanned aerial vehicle aerial survey river channel bank collapse process, the method is simple to operate and less in interference, and the time and labor cost can be greatly reduced.
In order to achieve the above object, according to an aspect of the present invention, there is provided a method for monitoring a river bank collapse process based on unmanned aerial vehicle aerial survey, including:
(1) sequentially arranging RTK point positions, and determining the flight height and the overlapping degree of the unmanned aerial vehicle through the bank slope width c and the bank slope height h of the parameter sample to obtain the aerial survey data of the unmanned aerial vehicle;
(2) processing the unmanned aerial vehicle aerial survey data by adopting image data processing software;
(3) acquiring the longitudinal crack length a, the longitudinal crack width b, the bank slope width c and the bank slope height h of a bank to be banked in a river reach through the processed aerial survey data of the unmanned aerial vehicle, and calculating the critical balanced soil volume and the critical slope;
(4) and calculating the topographic deviation DoD by using ArcMap software through multiple times of unmanned aerial vehicle aerial survey data.
Preferably, step (1) comprises:
The average value of the measured bank slope width c and the bank slope height h is used as the basis of the flight height and the overlapping degree of the unmanned aerial vehicle, the longitudinal crack width b of the concave bank is used as the limit value of the geometric minimum resolution of the unmanned aerial vehicle, and the bank slope height h is used as the limit value of the elevation data resolution after the image data processing of the unmanned aerial vehicle.
Preferably, step (2) comprises:
and carrying out classification extraction, three-dimensional orthoimage calculation and depth noise reduction on the unmanned aerial vehicle aerial survey data by using image data processing software, wherein the image data processing software comprises one or more combinations of Pix4D, Cloud company and ArcGIS.
Preferably, step (3) comprises:
calculating a three-dimensional terrain in a river bank range by utilizing the processed unmanned aerial vehicle aerial survey data and the RTK accurate positioning, performing DEM noise deep processing on the three-dimensional terrain by utilizing an ArcMap image processing tool to obtain parameters of a longitudinal crack length a of a concave bank, a longitudinal crack width b of the concave bank, a bank slope width c and a bank slope height h required by bank collapse monitoring, identifying a bank collapse critical state by using river bank parameter data and terrain data obtained by unmanned aerial vehicle aerial survey, and calculating a soil body volume and a critical gradient of critical balance.
Preferably, the critical equilibrium soil volume is calculated from V ═ a × c × h/3, and the critical gradient is calculated from β ═ ctan (h/c).
Preferably, step (4) comprises:
and generating a topographic deviation DoD by using an image processing tool of ArcMap through topographic data obtained after deep noise reduction by repeated aerial surveying before and after the flood season, and calculating the annual bank collapse volume of the river reach scale.
Preferably, in step (1), the order-placement RTK point location includes:
determining boundaries at an upstream section and a downstream section of the river reach, searching the highest point and the lowest point of the river reach, arranging RTK point locations on the determined boundaries, the highest point and the lowest point of the river reach, and supplementing the RTK point locations to the inflection points of the click terrain with more complex terrain in the river reach.
According to another aspect of the invention, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any of the above.
In general, compared with the prior art, the above technical solution contemplated by the present invention can achieve the following beneficial effects:
(1) the aerial survey geometric resolution acquired by the unmanned aerial vehicle can reach centimeter level, and the form and the longitudinal cracks of the river bank of the general river can be clearly identified;
(2) topographic data in different periods can be quickly and accurately obtained, and the topographic difference DoD generated by processing has high reliability;
(3) The critical balanced soil volume and the critical gradient are calculated simply;
(4) the numbering method of the RTK point positions is simple and convenient, can be rapidly identified, has low cost of used materials and is easy to obtain;
(5) the workload required by the whole field investigation and observation is small, and the aerial survey task of the river reach A can be completed by only 3 survey personnel at most.
(6) The method can save manpower, improve efficiency, obtain high-precision river bank parameters and topographic data, generate topographic deviation DoD, and calculate annual bank collapse volume of river reach dimensions, thereby realizing monitoring of the bank collapse process.
Drawings
Fig. 1 is a schematic flow chart of a monitoring method for aerial surveying a river bank collapse process based on an unmanned aerial vehicle according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an RTK dotting provided by an embodiment of the present invention;
fig. 3 is a schematic cross-sectional view of an unmanned aerial vehicle navigation riverside provided by an embodiment of the invention;
fig. 4 is a schematic aerial view of an unmanned aerial vehicle aerial survey river bank according to an embodiment of the present invention;
wherein, 1-lime powder fixed point, 2-concave bank slope, 3-longitudinal crack, 4-unmanned plane, 5-tripod head, and 6-camera.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The Unmanned Aerial Vehicle (UAV) technology has developed rapidly in recent years, and the Unmanned Aerial Vehicle Aerial survey has the advantages of being simple in operation, high in data acquisition quality, cost-saving and the like, and becomes another field measurement mode for efficiently acquiring high-resolution image data besides a remote sensing satellite. The invention provides a monitoring method based on the river bank collapse process of the aerial survey of an unmanned aerial vehicle by combining with a noise processing technology based on the aerial survey of topographic data of the unmanned aerial vehicle, which at least comprises the steps of obtaining the river bank parameters in the aerial survey river section of the unmanned aerial vehicle and generating the topographic difference DoD (DEM of difference). The method is simple to operate, less in interference, capable of greatly reducing time and labor cost, generating the topographic deviation DoD while acquiring the river bank parameters and the topography, and calculating the annual bank collapse volume of the river reach scale so as to monitor the bank collapse process of the river channel.
The method mainly aims at acquiring river bank parameters and topographic data in a river reach, and the used tools comprise a ruler, lime powder, an unmanned aerial vehicle and an RTK (Real-time kinematic). As shown in fig. 1, the method comprises the following steps:
s1: sequentially arranging RTK point positions, and determining the flight height and the overlapping degree of the unmanned aerial vehicle through the bank slope width c and the bank slope height h of the parameter sample to obtain the aerial survey data of the unmanned aerial vehicle;
As a preferred embodiment, step S1 can be implemented as follows:
the average value of the measured bank slope width c and the bank slope height h is used as the basis of the flight height and the overlapping degree of the unmanned aerial vehicle, the longitudinal crack width b of the concave bank is used as the limit value of the geometric minimum resolution of the unmanned aerial vehicle, and the bank slope height h is used as the limit value of the elevation data resolution after the image data processing of the unmanned aerial vehicle.
Specifically, firstly, determining a river reach (hereinafter referred to as river reach a) to be tested and measured and about to collapse, confirming a boundary of the river reach a, determining a boundary between an upstream section and a downstream section of the river reach a, scattering lime powder 1 at the boundary to fix a point, determining a plurality of points (such as the first 4 points numbered in sequence in fig. 2) of the boundary, sequentially searching the highest point, the lowest point, a terrain complex point (generally an inflection point of terrain height difference change) and a terrain inflection point of the river reach a flowing direction of a river, continuously scattering lime powder at the points (such as the last 2 points numbered in sequence in fig. 2), and sequentially numbering the points; RTK point locations are then placed for the points that have been numbered with the lime powder as shown in fig. 2.
Wherein, adopt lime powder serial number, because the lime powder fixed point can be clearly discerned in the image that produces when unmanned aerial vehicle carries out the orthophoto aerial photograph.
Further, since imaging in three-dimensional space requires XYZ (longitude (X), latitude (Y), and height (Z)) three-coordinate values, a selected river segment is sampled, and a cross-section river bank sample is randomly selected every several meters (e.g., 5m) along the direction of water flow in the riverbed. As shown in fig. 3 and 4, by randomly measuring the width c and the height h of the bank slope of a plurality of (e.g., 10) concave bank slopes 2, the average value of the width c and the height h of the bank slope is calculated and used as the basis of the flying height and the overlapping degree of the UAV 4. The width b of the longitudinal crack 3 of the concave bank is used as a limit value of the geometric minimum resolution of the UAV, and the height h of the bank slope is used as a limit value of the elevation data resolution after the UAV image data are processed. At present, the minimum safe flying height of the civil unmanned aerial vehicle is 20m and is more than 200m at most, the aerial survey geometric resolution corresponding to the flying height interval completely meets the monitoring of different river bank caving, and the minimum 5 cm-wide longitudinal crack can be clearly identified.
Then, the height and the overlapping degree in the range are grouped for trial flight, and a camera 6 is driven by a holder 5 to shoot; and under the condition that the flight attitude of the unmanned aerial vehicle meets the requirements that the errors of the roll angle, the course angle and the pitch angle are all +/-3 degrees, the Pix4Dmap software is used for evaluating the quality of the unmanned aerial vehicle image according to the RTK positioning data, the image data and the attitude data. And eliminating images which do not meet the mapping specification, generating a quality report, and finally determining the final flight height and the overlap degree according to the quality report.
After the flight height of the unmanned aerial vehicle and the image overlapping degree are determined, the unmanned aerial vehicle route of the river reach A can be confirmed.
Image data processing software such as Pix4D, Cloud company and ArcGIS are selected to simulate aerial triangulation and establish a flight band. A single model is established by calculating relative orientation elements and model point coordinates, model connection operation is carried out by utilizing common connection points between adjacent models to establish a navigation band three-dimensional model with a unified scale, and each navigation band model is independently established by each single navigation line. Each flight band model unit is roughly leveled and unified in a common coordinate system, and finally, integral adjustment calculation is carried out. Therefore, the nonlinear correction formulas of the aeroribbons are listed, and the nonlinear correction parameters of the aeroribbons are calculated according to the unified adjustment of the least square method criterion. In the calculation process, the ground coordinates of the homonymous connecting points between adjacent flight zones are equal, the internal coordinate of the control point is equal to the external actual measurement coordinate, and the sum of squares of coordinate correction numbers of each model point is minimum, so that the ground coordinates of the whole area network encryption points are finally obtained. The zone determined by the ground coordinates is the unmanned plane route.
S2: processing the aerial survey data of the unmanned aerial vehicle by adopting image data processing software;
as a preferred embodiment, step S2 can be implemented as follows:
and carrying out classification extraction, three-dimensional ortho image calculation, depth noise reduction and other processing on the unmanned aerial vehicle aerial survey data by using image data processing software comprising Pix4D, Cloud company and ArcGIS.
Further, unmanned aerial vehicle aerial survey data is processed: an unmanned aerial vehicle OrthophotoMap (DOM) is a result obtained after an unmanned aerial vehicle completes a flight task, preprocessing, geometric correction and mosaic are performed according to a pixel-by-pixel sequence by using an unmanned aerial vehicle aerial survey image photo, and an image set is cut in a certain image scale range and contains information of a sand wave form parameter to be measured. And the technologies of image filtering processing, lens distortion correction, geometric correction, relative orientation, absolute orientation, aerial triangulation, image registration and matching and the like are utilized to realize the rapid splicing of the images.
S3: acquiring the length a, the width b, the width c and the height h of a longitudinal crack of a bank to be banked in a river reach through the processed aerial survey data of the unmanned aerial vehicle, and calculating the volume and the critical gradient of a soil body with critical balance;
As a preferred embodiment, step S3 can be implemented as follows:
calculating a three-dimensional terrain in a river bank range by utilizing a processed UAV high-precision orthographic image set and RTK accurate positioning, performing DEM noise deep processing on the three-dimensional terrain by utilizing an ArcMap image processing tool to obtain parameters of a longitudinal crack length a of a concave bank, a longitudinal crack width b of the concave bank, a bank slope width c and a bank slope height h required by bank collapse monitoring, identifying a bank collapse critical state by using bank parameter data and terrain data obtained by aerial survey of an unmanned aerial vehicle, and calculating a soil volume formula of critical balance as V (a x c x h/3) and a critical gradient beta (h/c) according to that the cross section of a critical soil body is a triangle.
Specifically, an orthoimage set is calculated using Pix4D and Cloud compass according to certain parameter settings, taking into account the precise positioning of the RTK. The depth processing of DEM noise is carried out by using an ArcMap image processing tool, the river bank DEM data of the river bank A with high resolution and high precision is obtained by noise reduction, and the length a, the width b, the width c and the height h of the bank slope of the concave bank longitudinal crack of the river bank A are further obtained.
Further, identifying a bank collapse critical state according to the length a, the width b, the bank slope width c and the bank slope height h of the longitudinal crack of the concave bank of the river reach A acquired by the unmanned aerial vehicle, and calculating to obtain a critical balanced soil volume and a critical slope.
S4: and calculating the topographic deviation DoD by using ArcMap software through multiple times of unmanned aerial vehicle aerial survey data.
As a preferred embodiment, step S4 can be implemented as follows:
and (3) generating a topographic deviation DoD by using the topographic data after deep noise reduction obtained by repeated aerial surveying before and after the flood season and an image processing tool of ArcMap, and calculating the annual bank collapse volume of the river reach scale (within 3 km).
According to the method, the bank slope parameters of the river bank are obtained through aerial survey of the unmanned aerial vehicle, and the volume and the critical slope of the soil body with critical balance are calculated. And repeatedly carrying out aerial survey before and after the flood season by the unmanned aerial vehicle to generate DEM terrain variation, namely the annual bank collapse volume of the river reach scale.
It should be noted that, according to the implementation requirement, each step/component described in the present application can be divided into more steps/components, and two or more steps/components or partial operations of the steps/components can be combined into new steps/components to achieve the purpose of the present invention.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (8)

1. A monitoring method based on an unmanned aerial vehicle aerial survey river channel bank collapse process is characterized by comprising the following steps:
(1) sequentially arranging RTK point positions, and determining the flight height and the overlapping degree of the unmanned aerial vehicle through the bank slope width c and the bank slope height h of the parameter sample to obtain the aerial survey data of the unmanned aerial vehicle;
(2) processing the unmanned aerial vehicle aerial survey data by adopting image data processing software;
(3) acquiring the longitudinal crack length a, the longitudinal crack width b, the bank slope width c and the bank slope height h of a bank to be banked in a river reach through the processed aerial survey data of the unmanned aerial vehicle, and calculating the critical balanced soil volume and the critical slope;
(4) and calculating the topographic deviation DoD by using ArcMap software through multiple times of unmanned aerial vehicle aerial survey data.
2. The method of claim 1, wherein step (1) comprises:
the average value of the measured bank slope width c and the bank slope height h is used as the basis of the flight height and the overlapping degree of the unmanned aerial vehicle, the longitudinal crack width b of the concave bank is used as the limit value of the geometric minimum resolution of the unmanned aerial vehicle, and the bank slope height h is used as the limit value of the elevation data resolution after the image data processing of the unmanned aerial vehicle.
3. The method of claim 1, wherein step (2) comprises:
and carrying out classification extraction, three-dimensional orthoimage calculation and depth noise reduction on the unmanned aerial vehicle aerial survey data by using image data processing software, wherein the image data processing software comprises one or more combinations of Pix4D, Cloud company and ArcGIS.
4. The method of any one of claims 1 to 3, wherein step (3) comprises:
calculating a three-dimensional terrain in a river bank range by utilizing the processed unmanned aerial vehicle aerial survey data and the RTK accurate positioning, performing DEM noise deep processing on the three-dimensional terrain by utilizing an ArcMap image processing tool to obtain parameters of a longitudinal crack length a of a concave bank, a longitudinal crack width b of the concave bank, a bank slope width c and a bank slope height h required by bank collapse monitoring, identifying a bank collapse critical state by using river bank parameter data and terrain data obtained by unmanned aerial vehicle aerial survey, and calculating a soil body volume and a critical gradient of critical balance.
5. The method of claim 4, wherein the critical equilibrium soil volume is calculated from V ═ a x c x h/3 and the critical slope is calculated from β ═ ctan (h/c).
6. The method of claim 5, wherein step (4) comprises:
and generating a topographic deviation DoD by using an image processing tool of ArcMap through topographic data obtained after deep noise reduction by repeated aerial surveying before and after the flood season, and calculating the annual bank collapse volume of the river reach scale.
7. The method of claim 4, wherein in step (1), the arranging RTK point locations in order comprises:
Determining boundaries at an upstream section and a downstream section of the river reach, searching the highest point and the lowest point of the river reach, arranging RTK point locations on the determined boundaries, the highest point and the lowest point of the river reach, and supplementing the RTK point locations to the inflection points of the click terrain with more complex terrain in the river reach.
8. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
CN202010635673.XA 2020-07-03 2020-07-03 Unmanned aerial vehicle-based monitoring method for aerial survey river channel bank collapse process Pending CN111854699A (en)

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* Cited by examiner, † Cited by third party
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Citations (18)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104155995A (en) * 2014-08-11 2014-11-19 江苏恒创软件有限公司 Unmanned helicopter-based mining subsidence monitoring method
CN105464040A (en) * 2016-01-15 2016-04-06 武汉大学 Numerical computation method for alluvial river bank collapse process
CN105783878A (en) * 2016-03-11 2016-07-20 三峡大学 Small unmanned aerial vehicle remote sensing-based slope deformation detection and calculation method
CN106441057A (en) * 2016-08-25 2017-02-22 陕西省地质环境监测总站 Geological disaster relative displacement automatic measurement and image collecting early warning device
CN106969751A (en) * 2017-03-13 2017-07-21 西安科技大学 A kind of method of the coal mining subsidence amount monitoring calculation based on unmanned aerial vehicle remote sensing
CN107289894A (en) * 2017-06-26 2017-10-24 中国电建集团成都勘测设计研究院有限公司 A kind of three-dimensional method of quick determination reservoir bank slump scale
CN107757943A (en) * 2017-10-20 2018-03-06 江苏筑升土木工程科技有限公司 The reservoir stability stability long term monitoring apparatus and method of captive unmanned plane
CN108459318A (en) * 2018-02-02 2018-08-28 中国铁路设计集团有限公司 Potential landslide EARLY RECOGNITION method based on remote sensing technology
CN108955999A (en) * 2018-05-14 2018-12-07 武汉大学 A kind of Bank Failure real-time monitoring device based on pressure sensing technology
CN109242247A (en) * 2018-08-02 2019-01-18 西安科技大学 A kind of coal field surface collapse extent of the destruction evaluation method
CN109508508A (en) * 2018-12-08 2019-03-22 河北省地矿局国土资源勘查中心 Open-pit mine treatment and exploration design method
CN109541592A (en) * 2018-10-30 2019-03-29 长安大学 Loess Landslide type and sliding-modes analysis method based on InSAR multidimensional deformation data
CN209241303U (en) * 2018-12-18 2019-08-13 辽宁壮龙无人机科技有限公司 A kind of horn and unmanned plane of rotor wing unmanned aerial vehicle
CN110470275A (en) * 2019-09-02 2019-11-19 长沙理工大学 A method of withered riverbed bed ripples morphological parameters are measured based on UAV aerial survey terrain data
CN110514113A (en) * 2019-06-13 2019-11-29 杭州电子科技大学 A kind of monitoring land slide slit method based on monocular vision camera
CN110542708A (en) * 2019-09-29 2019-12-06 长江勘测规划设计研究有限责任公司 landslide early warning system and method
CN111142119A (en) * 2020-01-10 2020-05-12 中国地质大学(北京) Mine geological disaster dynamic identification and monitoring method based on multi-source remote sensing data
CN111337997A (en) * 2020-03-17 2020-06-26 成都理工大学 Method for quickly identifying potential collapse disaster body

Patent Citations (18)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104155995A (en) * 2014-08-11 2014-11-19 江苏恒创软件有限公司 Unmanned helicopter-based mining subsidence monitoring method
CN105464040A (en) * 2016-01-15 2016-04-06 武汉大学 Numerical computation method for alluvial river bank collapse process
CN105783878A (en) * 2016-03-11 2016-07-20 三峡大学 Small unmanned aerial vehicle remote sensing-based slope deformation detection and calculation method
CN106441057A (en) * 2016-08-25 2017-02-22 陕西省地质环境监测总站 Geological disaster relative displacement automatic measurement and image collecting early warning device
CN106969751A (en) * 2017-03-13 2017-07-21 西安科技大学 A kind of method of the coal mining subsidence amount monitoring calculation based on unmanned aerial vehicle remote sensing
CN107289894A (en) * 2017-06-26 2017-10-24 中国电建集团成都勘测设计研究院有限公司 A kind of three-dimensional method of quick determination reservoir bank slump scale
CN107757943A (en) * 2017-10-20 2018-03-06 江苏筑升土木工程科技有限公司 The reservoir stability stability long term monitoring apparatus and method of captive unmanned plane
CN108459318A (en) * 2018-02-02 2018-08-28 中国铁路设计集团有限公司 Potential landslide EARLY RECOGNITION method based on remote sensing technology
CN108955999A (en) * 2018-05-14 2018-12-07 武汉大学 A kind of Bank Failure real-time monitoring device based on pressure sensing technology
CN109242247A (en) * 2018-08-02 2019-01-18 西安科技大学 A kind of coal field surface collapse extent of the destruction evaluation method
CN109541592A (en) * 2018-10-30 2019-03-29 长安大学 Loess Landslide type and sliding-modes analysis method based on InSAR multidimensional deformation data
CN109508508A (en) * 2018-12-08 2019-03-22 河北省地矿局国土资源勘查中心 Open-pit mine treatment and exploration design method
CN209241303U (en) * 2018-12-18 2019-08-13 辽宁壮龙无人机科技有限公司 A kind of horn and unmanned plane of rotor wing unmanned aerial vehicle
CN110514113A (en) * 2019-06-13 2019-11-29 杭州电子科技大学 A kind of monitoring land slide slit method based on monocular vision camera
CN110470275A (en) * 2019-09-02 2019-11-19 长沙理工大学 A method of withered riverbed bed ripples morphological parameters are measured based on UAV aerial survey terrain data
CN110542708A (en) * 2019-09-29 2019-12-06 长江勘测规划设计研究有限责任公司 landslide early warning system and method
CN111142119A (en) * 2020-01-10 2020-05-12 中国地质大学(北京) Mine geological disaster dynamic identification and monitoring method based on multi-source remote sensing data
CN111337997A (en) * 2020-03-17 2020-06-26 成都理工大学 Method for quickly identifying potential collapse disaster body

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
李志威: "基于无人机航测黄河源弯曲河道泥沙亏损量计算", 《水科学进展》 *
杨涵苑 等: "基于流量过程的若尔盖黑河下游崩岸规律研究", 《水力发电学报》 *
赵宇: "无人机倾斜摄影技术在高边坡测量中的应用研究", 《智慧地球》 *
陈飞: "《长江流域地址灾害及防治》", 31 July 2007, 长江出版社 *
韦博文 等: "基于改进的MF-FDOG算法和无人机影像提取黄土地区地裂缝", 《测绘》 *

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113177693A (en) * 2021-04-06 2021-07-27 长江水利委员会水文局长江下游水文水资源勘测局(长江水利委员会水文局长江下游水环境监测中心) Landslide monitoring and early warning assessment method based on analytic hierarchy process
CN113177693B (en) * 2021-04-06 2024-06-11 长江水利委员会水文局长江下游水文水资源勘测局(长江水利委员会水文局长江下游水环境监测中心) Method for monitoring, early warning and evaluating bank collapse based on analytic hierarchy process
CN114659490A (en) * 2022-01-27 2022-06-24 江苏省水利科学研究院 Real-time monitoring and bank-caving early warning method for displacement of underwater bank slope at bank-caving easy-to-occur section
CN115439672A (en) * 2022-11-04 2022-12-06 浙江大华技术股份有限公司 Image matching method, illicit detection method, terminal device, and storage medium
WO2024195484A1 (en) * 2023-03-23 2024-09-26 ソニーセミコンダクタソリューションズ株式会社 Information processing device, information processing method, and program
CN117541068A (en) * 2024-01-10 2024-02-09 武汉华测卫星技术有限公司 Unmanned ship-based bank collapse risk assessment method and system
CN117541068B (en) * 2024-01-10 2024-04-02 武汉华测卫星技术有限公司 Unmanned ship-based bank collapse risk assessment method and system
CN117877213A (en) * 2024-03-13 2024-04-12 江苏省水利科学研究院 Real-time monitoring and early warning system and method for bank collapse based on acoustic sensor

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