CN112344913A - Regional risk coefficient evaluation method by utilizing oblique photography image of unmanned aerial vehicle - Google Patents
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- G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
- G01C11/04—Interpretation of pictures
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- B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
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Abstract
The invention discloses a regional risk coefficient evaluation method by utilizing an unmanned aerial vehicle oblique photography image. Compared with the traditional method, the method utilizes the original photo data to detect the landslide, so that the detection rate of the landslide is higher, and the danger coefficient of a detection area can be output.
Description
Technical Field
The invention belongs to the field of geological exploration, and relates to a regional risk coefficient evaluation method by utilizing an unmanned aerial vehicle oblique photography image.
Background
At present, the traditional method for interpreting geological disasters from unmanned aerial vehicle images is to perform dense matching on the images to obtain DOM and DSM, and then perform splicing and digital correction to obtain an orthoimage. And then, aiming at the ortho-image, detecting a geological disaster area in the ortho-image by using technical means such as dependent feature design and machine learning, and then finishing area safety evaluation. This conventional method has the following drawbacks: when the geological disaster is positioned on a slope with a larger gradient, the area of the area on the orthographic projection image is smaller, the disaster characteristic is not obvious, and the omission is easily caused; the background of the geological disaster area is complex, which can cause the detection difficulty to increase. After traditional detection, only the area of geological disaster can be obtained, and the overall risk coefficient of the monitored area is difficult to evaluate.
Disclosure of Invention
In order to solve the problems, the invention provides a regional risk coefficient evaluation method by utilizing an unmanned aerial vehicle oblique photography image, which is used for detecting a geological disaster region from the oblique photography image by utilizing a depth learning image segmentation method, solving the real geographical position of the disaster region based on accurate external parameters obtained by adjustment by an unmanned aerial vehicle image beam method, and analyzing the risk coefficient of the monitoring region by combining the area and gradient data of the detected disaster.
The invention relates to a regional risk coefficient evaluation method by utilizing an unmanned aerial vehicle oblique photography image, which comprises the following steps:
step 1, obtaining an unmanned aerial vehicle oblique photography image and accurate external parameters of each image obtained by an unmanned aerial vehicle POS system.
Step 2: pixel coordinates of a geological disaster contour are extracted from original image data of an oblique photographic image.
And step 3: and (3) converting the geological disaster outline pixel coordinates into image space coordinates, and converting the image space coordinates into image space auxiliary coordinates by combining the external parameters in the step (1).
And 4, step 4: and acquiring a three-dimensional surface mesh model of the measurement area.
And 5: and calculating the three-dimensional world coordinate of the disaster in each image to obtain the distribution and the total number of the geological disasters in the whole area under the three-dimensional world coordinate system.
Step 6: and establishing a gradient model of the measuring area according to the generated three-dimensional surface grid model of the measuring area, and further calculating the risk coefficient of the area.
The invention has the advantages that:
1. the invention utilizes an area danger coefficient evaluation method of an unmanned aerial vehicle oblique photography image and utilizes the unmanned aerial vehicle to detect a plurality of original photos collected by the landslide area. Then, the corresponding relation between the landslide area and the geographic coordinate system under the pixel coordinate system is established, so that the direct detection of the geological disaster of the oblique photography image of the unmanned aerial vehicle is realized
2. Compared with the traditional method, the regional risk coefficient evaluation method based on the oblique photography image of the unmanned aerial vehicle detects the landslide by using the original photo data, so that the detection rate of the landslide is higher.
3. The regional risk coefficient evaluation method utilizing the oblique photography image of the unmanned aerial vehicle is high in accuracy, more obvious in disaster outline and clearer in texture. Meanwhile, the image is segmented based on the multi-scale full convolution neural network considering the context information, so that the complex background interference on the image can be effectively reduced, the geological disasters of different scales can be adapted, and the accuracy of contour extraction is higher.
4. According to the method for evaluating the regional danger coefficient by using the oblique photography image of the unmanned aerial vehicle, the robustness is high, the oblique photography image can be used for carrying out more targeted image acquisition aiming at a region with a larger gradient, the geological disaster contour is directly extracted from the oblique photography original image, so that the texture of the geological disaster in the region with the larger gradient is more obvious, the texture information cannot be lost due to orthoscopic correction, and the accuracy of contour extraction is favorably improved. And the convolutional neural network has strong generalization capability, so that the robustness of geological disaster extraction is higher, and the omission factor of the geological disaster is reduced.
5. According to the regional risk coefficient evaluation method based on the unmanned aerial vehicle oblique photography image, the risk coefficient of the measured region is calculated based on the geological disaster range and gradient data obtained through calculation, and a data basis can be provided for the targeted management of the disaster region.
Drawings
Fig. 1 is a flowchart of a method for evaluating a risk factor of a region using oblique photography of an unmanned aerial vehicle according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, the method for evaluating the risk factor of a region using oblique images of an unmanned aerial vehicle according to the present invention includes the following steps:
s101, performing homonymous point matching, forward intersection and bundle adjustment on the overlapped region of the unmanned aerial vehicle oblique photography image and the pose of each image acquired by the unmanned aerial vehicle POS system, thereby acquiring accurate external parameters (line elements and angle elements) of each image.
S102: aiming at the original image data of the oblique photography image in the step 1, a multi-scale full convolution neural network considering context information is adopted to carry out image segmentation on the image, so that the pixel coordinates of the geological disaster outline are accurately extracted. The neural network is added with a weighted attention mechanism module aiming at geological disaster detection, firstly, the neural network is used for extracting the remote sensing image characteristics containing landslide, and then, the weighting operation is carried out on the image characteristics in the neural network intermediate layer:
wherein the content of the first and second substances,
representing a weighted feature; x is the number ofiRepresenting the output characteristics of the network middle layer; n is the product of the length and the width of the output characteristic of the network middle layer; g1(xi),g2(xj) And g3(xi) Each represents a convolution operation using a different 1 x 1 convolution kernel on the input features. The landslide feature is given a higher weight through weighting operation, and an irrelevant background is given a lower weight, so that the interference caused by the background is suppressed. A weighting attention mechanism module aiming at detecting geological disasters is added into a neural network, and the context information of the image is fully considered, so that the interference of complex backgrounds (such as bare land, trees, riverways and the like) except the geological disasters in the image is inhibited. After detection, the number of geological disasters in each image and the positions of the geological disasters in the pixel coordinate system can be obtained.
S103: and (3) converting the geological disaster outline pixel coordinates into image space coordinates by combining the internal parameters and distortion parameters of the unmanned aerial vehicle camera, and converting the angle element information in the external parameters in the step (1) into image space auxiliary coordinates.
S104: and (3) performing region measurement dense reconstruction on each image external parameter obtained in the step (1) to obtain three-dimensional point cloud, and performing Poisson reconstruction on the point cloud to obtain a three-dimensional surface mesh model of the region measurement.
S105: according to the condition that three points of the unmanned aerial vehicle camera optical center, the contour points are collinear at the image plane position and the real three-dimensional positions of the contour points, a ray is formed by each contour point at the image plane position and the corresponding camera optical center, the ray takes the three-dimensional world coordinates of the camera optical center as an end point, a vector formed by the origin of the image space auxiliary coordinates to the image space auxiliary coordinates of the contour points as a direction, the intersection point of the ray formed by each disaster contour point and the three-dimensional surface model obtained in the step 4 is calculated, and the three-dimensional world coordinates of the intersection point are the real three-dimensional world coordinates of the geological disaster points. And calculating the three-dimensional world coordinate of the disaster in each image to obtain the distribution and the total number of the geological disasters in the whole area under the three-dimensional world coordinate system.
S106: firstly, establishing a gradient model of a measuring area according to a generated three-dimensional surface grid model of the measuring area; then, making n geological disasters exist in the region, and counting the projection area A of each geological disaster regioni(i ═ 1,2, …, n), maximum slope in the region
Mean slope in area
90 th percentile of slope within a zone
And the area A of the entire monitoring regionall(ii) a Obtaining the risk coefficient D of the area:
in the formula, w1、w2、w3The weights for the influence of the average slope value, the maximum slope value and the slope characteristic on the landslide are respectively. Function(s)
e is a natural constant.
Claims (4)
1. A regional risk coefficient assessment method utilizing unmanned aerial vehicle oblique photography images is characterized in that: the method comprises the following steps:
step 1, acquiring an unmanned aerial vehicle oblique photography image and accurate external parameters of each image acquired by an unmanned aerial vehicle POS system;
step 2: extracting pixel coordinates of a geological disaster outline from original image data of the oblique photography image;
and step 3: converting the geological disaster outline pixel coordinates into image space coordinates, and converting the image space coordinates into image space auxiliary coordinates by combining the external parameters in the step 1;
and 4, step 4: acquiring a three-dimensional surface mesh model of a measurement area;
and 5: calculating the three-dimensional world coordinate of the disaster in each image to obtain the distribution and the total number of the geological disasters in the whole area under a three-dimensional world coordinate system;
step 6: and establishing a gradient model of the measuring area according to the generated three-dimensional surface grid model of the measuring area, and further calculating the risk coefficient of the area.
2. The method of claim 1, wherein the area risk coefficient evaluation method using the oblique-view image of the drone is: and 2, extracting pixel coordinates of the image segmentation geological disaster outline by adopting a multi-scale full convolution neural network considering context information.
3. The method of claim 2, wherein the area risk coefficient evaluation method using the oblique-view image of the drone is: the neural network is added with a weighted attention mechanism module aiming at geological disaster detection, firstly, the neural network is used for extracting the remote sensing image characteristics containing landslide, and then the weighting operation is carried out on the image characteristics in the neural network intermediate layer:
wherein the content of the first and second substances,
representing a weighted feature; x is the number ofiRepresenting the output characteristics of the network middle layer; n being output characteristics of intermediate layers of the networkThe product of the length and the width; g1(xi),g2(xj) And g3(xi) Each represents a convolution operation using a different 1 x 1 convolution kernel on the input features.
4. The method of claim 1, wherein the area risk coefficient evaluation method using the oblique-view image of the drone is: in step 6, making n geological disasters exist in the region, and counting the projection area A of each geological disaster regioni(i ═ 1,2, …, n), maximum slope in the region
Average slope in area
90 th percentile of slope within a zone
And the area A of the entire monitoring regionall(ii) a The risk factor D of the area is calculated from the above parameters:
in the formula, w1、w2、w3Respectively weighing the influence of the average gradient value, the maximum gradient value and the gradient characteristic on the landslide;
e is a natural constant.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113012398A (en) * | 2021-02-20 | 2021-06-22 | 中煤航测遥感集团有限公司 | Geological disaster monitoring and early warning method and device, computer equipment and storage medium |
| CN114881457A (en) * | 2022-05-05 | 2022-08-09 | 中咨数据有限公司 | Decision tree-based landslide and collapse disaster classification method and electronic equipment |
| CN114881454A (en) * | 2022-05-05 | 2022-08-09 | 中咨数据有限公司 | Mountain landslide and collapse disaster classification method and electronic equipment |
| CN115358486A (en) * | 2022-09-20 | 2022-11-18 | 中咨数据有限公司 | Port freight volume prediction method, system and application based on three-dimensional satellite image |
| CN116182800A (en) * | 2023-02-23 | 2023-05-30 | 广州市峻泰工程检测技术有限公司 | Slope monitoring system based on unmanned aerial vehicle oblique photography technology |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150292887A1 (en) * | 2014-04-14 | 2015-10-15 | SAAB Vricon System AB | Navigation based on at least one sensor and a three dimensional map |
| CN105427377A (en) * | 2015-11-03 | 2016-03-23 | 武汉地大信息工程股份有限公司 | Unmanned plane based geological disaster data collection processing method and device |
| CN107860367A (en) * | 2017-11-03 | 2018-03-30 | 广州地理研究所 | A kind of Group-occurring landslides volume rapid extracting method based on low latitude unmanned aerial vehicle remote sensing |
| CN108344397A (en) * | 2017-12-28 | 2018-07-31 | 中国公路工程咨询集团有限公司 | Automation modeling method, system and its auxiliary device based on oblique photograph technology |
| CN109242344A (en) * | 2018-09-30 | 2019-01-18 | 青岛理工大学 | The multi-factor comprehensive measuring method of Regional Landslide calamity source |
| CN110008854A (en) * | 2019-03-18 | 2019-07-12 | 中交第二公路勘察设计研究院有限公司 | Unmanned plane image Highway Geological Disaster recognition methods based on pre-training DCNN |
| CN110706273A (en) * | 2019-08-21 | 2020-01-17 | 成都携恩科技有限公司 | Real-time collapse area measuring method based on unmanned aerial vehicle |
| KR20200059521A (en) * | 2018-11-21 | 2020-05-29 | 대한민국(산림청 국립산림과학원장) | Apparatus and Method for Automatically Detecting Landslide Area |
| CN111504268A (en) * | 2020-04-22 | 2020-08-07 | 深圳市地质局 | Intelligent early warning and forecasting method for dangerous case of soil slope |
-
2020
- 2020-11-09 CN CN202011240063.6A patent/CN112344913B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150292887A1 (en) * | 2014-04-14 | 2015-10-15 | SAAB Vricon System AB | Navigation based on at least one sensor and a three dimensional map |
| CN105427377A (en) * | 2015-11-03 | 2016-03-23 | 武汉地大信息工程股份有限公司 | Unmanned plane based geological disaster data collection processing method and device |
| CN107860367A (en) * | 2017-11-03 | 2018-03-30 | 广州地理研究所 | A kind of Group-occurring landslides volume rapid extracting method based on low latitude unmanned aerial vehicle remote sensing |
| CN108344397A (en) * | 2017-12-28 | 2018-07-31 | 中国公路工程咨询集团有限公司 | Automation modeling method, system and its auxiliary device based on oblique photograph technology |
| CN109242344A (en) * | 2018-09-30 | 2019-01-18 | 青岛理工大学 | The multi-factor comprehensive measuring method of Regional Landslide calamity source |
| KR20200059521A (en) * | 2018-11-21 | 2020-05-29 | 대한민국(산림청 국립산림과학원장) | Apparatus and Method for Automatically Detecting Landslide Area |
| CN110008854A (en) * | 2019-03-18 | 2019-07-12 | 中交第二公路勘察设计研究院有限公司 | Unmanned plane image Highway Geological Disaster recognition methods based on pre-training DCNN |
| CN110706273A (en) * | 2019-08-21 | 2020-01-17 | 成都携恩科技有限公司 | Real-time collapse area measuring method based on unmanned aerial vehicle |
| CN111504268A (en) * | 2020-04-22 | 2020-08-07 | 深圳市地质局 | Intelligent early warning and forecasting method for dangerous case of soil slope |
Non-Patent Citations (1)
| Title |
|---|
| 杨璇等: "基于卷积神经网络的高分影像公路勘察信息提取研究", 《中国交通信息化》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113012398A (en) * | 2021-02-20 | 2021-06-22 | 中煤航测遥感集团有限公司 | Geological disaster monitoring and early warning method and device, computer equipment and storage medium |
| CN114881457A (en) * | 2022-05-05 | 2022-08-09 | 中咨数据有限公司 | Decision tree-based landslide and collapse disaster classification method and electronic equipment |
| CN114881454A (en) * | 2022-05-05 | 2022-08-09 | 中咨数据有限公司 | Mountain landslide and collapse disaster classification method and electronic equipment |
| CN114881457B (en) * | 2022-05-05 | 2023-08-25 | 中咨数据有限公司 | Landslide and collapse disaster classification method based on decision tree and electronic equipment |
| WO2023193823A1 (en) * | 2022-05-05 | 2023-10-12 | 中咨数据有限公司 | Disaster classification method for landslides and collapse, and electronic device |
| CN115358486A (en) * | 2022-09-20 | 2022-11-18 | 中咨数据有限公司 | Port freight volume prediction method, system and application based on three-dimensional satellite image |
| CN116182800A (en) * | 2023-02-23 | 2023-05-30 | 广州市峻泰工程检测技术有限公司 | Slope monitoring system based on unmanned aerial vehicle oblique photography technology |
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