CN110440761A - A kind of processing method of unmanned plane aerophotogrammetry data - Google Patents
A kind of processing method of unmanned plane aerophotogrammetry data Download PDFInfo
- Publication number
- CN110440761A CN110440761A CN201910878915.5A CN201910878915A CN110440761A CN 110440761 A CN110440761 A CN 110440761A CN 201910878915 A CN201910878915 A CN 201910878915A CN 110440761 A CN110440761 A CN 110440761A
- Authority
- CN
- China
- Prior art keywords
- data
- point
- unmanned plane
- processing method
- dom
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
-
- G—PHYSICS
- G01—MEASURING; TESTING
- 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
Abstract
The invention discloses a kind of processing methods of unmanned plane aerophotogrammetry data, belong to technical field of data processing；It is the following steps are included: 1, camera lens distortion correction；2, characteristic point relative positioning；3, aerial triangulation；4, adjustment resolving is carried out；5, DSM data produces；6, discrete DSM data obtains point cloud data to production DOM data simultaneously；7, culture point classification processing is carried out to point cloud data；8, in conjunction with the point cloud data and DOM data production DLG data Jing Guo Fen Lei；9, DLG data are checked in the industry；The improved treatment process of the present invention can effectively improve production efficiency, reduce production cost.
Description
Technical field
The present invention relates to technical field of data processing, and in particular to a kind of processing side of unmanned plane aerophotogrammetry data
Method.
Background technique
The conventional resulting data processing of aerophotogrammetry, typically first passes sequentially through interior orientation --- and it is relatively fixed
To --- three steps of absolute orientation generate DOM and DSM, carry out the production of DLG data followed by stereoplotting equipment, but
The duty cycle of this scheme is longer, and equipment investment amount is big, higher cost, so one kind can save equipment investment, shortens life
The period is produced, increasing economic efficiency is urgently needed at present to the processing method of aerophotogrammetry data.
Summary of the invention
The technical problems to be solved by the present invention are: a kind of processing method of unmanned plane aerophotogrammetry data is provided,
Longer duty cycle with the processing method for solving currently data obtained to aeroplane photography, equipment investment amount is big, higher cost
Problem.
To solve the above problems, the present invention provides the following technical scheme that
A kind of processing method of unmanned plane aerophotogrammetry data, comprising the following steps:
S1, non-measured type camera mounted to unmanned plane obtain lens distortion parameter after carrying out calibration, and aviation flight are clapped
The photo data and lens distortion parameter taken the photograph bring in calibration model the image obtained after distortion correction into；
S2, the means progress combined with pyramid image matching is matched using SIFT feature to the image Jing Guo distortion correction certainly
Feature Points Matching is moved to complete relative positioning；
S3, by complete relative orientation after image data combination pos data and ground photo control point coordinate data, pass through human-computer interaction
Point is pierced, aerial triangulation operation is completed；
S4, adjustment resolving is carried out to measuring resulting photo control point data in step S3, and carry out accuracy test；
S5, DSM data will be produced on the basis of absolute orientation by the photo control point data of bundle adjustment processing；
S6, acquisition DOM data are corrected by carrying out differential to DSM data, and using DSM data as primary data production cloud number
According to；
S7, identification model is distinguished by the LiDAR atural object based on TerraScan to the progress culture point of the point transport in step S6
Classification processing；
S8, sorted landform point cloud data importing Become the picture software will be passed through in the DOM data obtained in step S6 and step S8
In, and carry out DLG data production；
S9 carries out quality examination in industry to DLG data and rectifies and improves.
Preferably, first entire to being covered on the photo control point of all base area faces when carrying out human-computer interaction thorn point in step s3
5 ~ 8 photo control points for surveying area are measured, then the Position Approximate of other phased points is found by predicting, are put with accelerating thorn
Journey.
Preferably, it is bundle adjustment model that used adjustment, which resolves model, in step s 4, after the completion of resolving also
Need to carry out calculation result evaluation according to error in photo control point, modification or rejecting are unsatisfactory for desired photo control point.
Preferably, the culture point data obtained in the step s 7 include vegetation point, building construction point, highway point, power line
Point data.
Preferably, the screening of culture point attribute data is carried out to DOM data obtained in step S6, and obtained with screening
Atural object point data is as DOM data used in step S8.
Preferably, used Become the picture software is any one in arcgis, cass or Tsing-Hua University mountain dimension in step s 8.
Preferably, in step s 9 to DLG data carry out industry in quality examination include position precision, mathematical accuracy,
Spatial frame of reference, integrality and logical consistency.
The invention has the advantages that:
Processing method provided by the invention to measurement data is mainly discrete by carrying out DSM data after obtaining DSM data
Change to obtain point cloud data, and by combining reproduction DLG number with DSM data after carrying out culture point classification to point cloud data
According to, by this improvement can: improve production efficiency, production link is completely embedded, transitions smooth, saves the time；Reduce life
Cost is produced, stereoplotting equipment is not needed to buy, point cloud data is directly produced according to DSM.
Detailed description of the invention
Fig. 1 is the flow chart that the data processing of unmanned plane aerophotogrammetry is carried out in the present embodiment.
Specific embodiment
With reference to the accompanying drawing and the present invention is described further in specific embodiment:
Embodiment:
Referring to Fig.1, the present embodiment provides a kind of processing methods of unmanned plane aerophotogrammetry data, comprising the following steps:
S1, non-measured type camera mounted to unmanned plane obtain lens distortion parameter after carrying out calibration, and aviation flight are clapped
The photo data and lens distortion parameter taken the photograph bring in calibration model the image obtained after distortion correction into；
S2, the means progress combined with pyramid image matching is matched using SIFT feature to the image Jing Guo distortion correction certainly
Feature Points Matching is moved to complete relative positioning；
S3, by complete relative orientation after image data combination pos data and ground photo control point coordinate data, pass through human-computer interaction
Point is pierced, aerial triangulation operation is completed；
S4, adjustment resolving is carried out to measuring resulting photo control point data in step S3, and carry out accuracy test；
S5, DSM data will be produced on the basis of absolute orientation by the photo control point data of bundle adjustment processing；
S6, acquisition DOM data are corrected by carrying out differential to DSM data, and using DSM data as primary data production cloud number
According to；
S7, identification model is distinguished by the LiDAR atural object based on TerraScan to the progress culture point of the point transport in step S6
Classification processing；
S8, sorted landform point cloud data importing Become the picture software will be passed through in the DOM data obtained in step S6 and step S8
In, and carry out DLG data production；
S9 carries out quality examination in industry to DLG data and rectifies and improves.
Quality examination includes position precision, mathematical accuracy, georeferencing in the industry carried out in step s 9 to DLG data
System, integrality and logical consistency.
It is bundle adjustment model that used adjustment, which resolves model, in step s 4, also needs basis after the completion of resolving
Error carries out calculation result evaluation in photo control point, and modification or rejecting are unsatisfactory for desired photo control point.
The culture point data obtained in the step s 7 include vegetation point, building construction point, highway point, power line point data.
The screening of culture point attribute data is carried out to DOM data obtained in step S6, and to screen obtained culture point
Data are as DOM data used in step S8.
Used Become the picture software is any one in arcgis, cass or Tsing-Hua University mountain dimension in step s 8.
Quality examination includes position precision, mathematical accuracy, georeferencing in the industry carried out in step s 9 to DLG data
System, integrality and logical consistency.
Claims (7)
1. a kind of processing method of unmanned plane aerophotogrammetry data, it is characterised in that: the following steps are included:
S1, non-measured type camera mounted to unmanned plane obtain lens distortion parameter after carrying out calibration, and aviation flight are clapped
The photo data and lens distortion parameter taken the photograph bring in calibration model the image obtained after distortion correction into；
S2, the means progress combined with pyramid image matching is matched using SIFT feature to the image Jing Guo distortion correction certainly
Feature Points Matching is moved to complete relative positioning；
S3, by complete relative orientation after image data combination pos data and ground photo control point coordinate data, pass through human-computer interaction
Point is pierced, aerial triangulation operation is completed；
S4, adjustment resolving is carried out to measuring resulting photo control point data in step S3, and carry out accuracy test；
S5, DSM data will be produced on the basis of absolute orientation by the photo control point data of bundle adjustment processing；
S6, acquisition DOM data are corrected by carrying out differential to DSM data, and using DSM data as primary data production cloud number
According to；
S7, identification model is distinguished by the LiDAR atural object based on TerraScan to the progress culture point of the point transport in step S6
Classification processing；
S8, sorted landform point cloud data importing Become the picture software will be passed through in the DOM data obtained in step S6 and step S8
In, and carry out DLG data production；
S9 carries out quality examination in industry to DLG data and rectifies and improves.
2. a kind of processing method of unmanned plane aerophotogrammetry data according to claim 1, it is characterised in that: in step
When carrying out human-computer interaction thorn point in rapid S3, first to covered on the photo control point of all base area faces entire 5 ~ 8 photo control points for surveying area into
Row measures, then the Position Approximate of other phased points is found by predicting, to accelerate to pierce point process.
3. a kind of processing method of unmanned plane aerophotogrammetry data according to claim 1, it is characterised in that: in step
It is bundle adjustment model that adjustment used in rapid S4, which resolves model, is also needed after the completion of resolving according to error in photo control point
Calculation result evaluation is carried out, modification or rejecting are unsatisfactory for desired photo control point.
4. a kind of processing method of unmanned plane aerophotogrammetry data according to claim 1, it is characterised in that: in step
The culture point data obtained in rapid S7 include vegetation point, building construction point, highway point, power line point data.
5. a kind of processing method of unmanned plane aerophotogrammetry data according to claim 1, it is characterised in that: to step
DOM data obtained in rapid S6 carry out the screening of culture point attribute data, and to screen obtained atural object point data as step
DOM data used in S8.
6. a kind of processing method of unmanned plane aerophotogrammetry data according to claim 1, it is characterised in that: in step
Become the picture software used in rapid S8 is any one in arcgis, cass or Tsing-Hua University mountain dimension.
7. a kind of processing method of unmanned plane aerophotogrammetry data according to claim 1, it is characterised in that: in step
In the industry carried out in rapid S9 to DLG data quality examination include position precision, mathematical accuracy, spatial frame of reference, integrality and
Logical consistency.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910878915.5A CN110440761B (en) | 2019-09-18 | 2019-09-18 | Processing method of aerial photogrammetry data of unmanned aerial vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910878915.5A CN110440761B (en) | 2019-09-18 | 2019-09-18 | Processing method of aerial photogrammetry data of unmanned aerial vehicle |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110440761A true CN110440761A (en) | 2019-11-12 |
| CN110440761B CN110440761B (en) | 2022-05-03 |
Family
ID=68440252
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910878915.5A Active CN110440761B (en) | 2019-09-18 | 2019-09-18 | Processing method of aerial photogrammetry data of unmanned aerial vehicle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110440761B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110986888A (en) * | 2019-12-17 | 2020-04-10 | 中煤航测遥感集团有限公司 | Aerial photography integrated method |
| CN111678502A (en) * | 2020-06-09 | 2020-09-18 | 中国科学院东北地理与农业生态研究所 | Method for extracting frozen soil disaster information based on unmanned aerial vehicle aerial survey image |
| CN111707620A (en) * | 2020-06-11 | 2020-09-25 | 中国电建集团华东勘测设计研究院有限公司 | Classification rule set for land utilization and water and soil loss monitoring method and system |
| CN113496461A (en) * | 2020-03-18 | 2021-10-12 | 广州极飞科技股份有限公司 | Point cloud data processing method and device, computer equipment and storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090154793A1 (en) * | 2007-12-17 | 2009-06-18 | Electronics And Telecommunications Research Institute | Digital photogrammetric method and apparatus using intergrated modeling of different types of sensors |
| CN101777189A (en) * | 2009-12-30 | 2010-07-14 | 武汉大学 | Method for measuring image and inspecting quantity under light detection and ranging (LiDAR) three-dimensional environment |
| CN102506824A (en) * | 2011-10-14 | 2012-06-20 | 航天恒星科技有限公司 | Method for generating digital orthophoto map (DOM) by urban low altitude unmanned aerial vehicle |
| CN102662179A (en) * | 2012-05-18 | 2012-09-12 | 四川省科学城久利科技实业有限责任公司 | Three-dimensional optimizing route selection method based on airborne laser radar |
| CN104457710A (en) * | 2014-12-15 | 2015-03-25 | 重庆市勘测院 | Digital photogrammetry method based on non-metric digital camera |
| CN108548525A (en) * | 2018-06-14 | 2018-09-18 | 浙江鼎测地理信息技术有限公司 | A method of carrying out field mapping using unmanned plane aeroplane photography |
-
2019
- 2019-09-18 CN CN201910878915.5A patent/CN110440761B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090154793A1 (en) * | 2007-12-17 | 2009-06-18 | Electronics And Telecommunications Research Institute | Digital photogrammetric method and apparatus using intergrated modeling of different types of sensors |
| CN101777189A (en) * | 2009-12-30 | 2010-07-14 | 武汉大学 | Method for measuring image and inspecting quantity under light detection and ranging (LiDAR) three-dimensional environment |
| CN102506824A (en) * | 2011-10-14 | 2012-06-20 | 航天恒星科技有限公司 | Method for generating digital orthophoto map (DOM) by urban low altitude unmanned aerial vehicle |
| CN102662179A (en) * | 2012-05-18 | 2012-09-12 | 四川省科学城久利科技实业有限责任公司 | Three-dimensional optimizing route selection method based on airborne laser radar |
| CN104457710A (en) * | 2014-12-15 | 2015-03-25 | 重庆市勘测院 | Digital photogrammetry method based on non-metric digital camera |
| CN108548525A (en) * | 2018-06-14 | 2018-09-18 | 浙江鼎测地理信息技术有限公司 | A method of carrying out field mapping using unmanned plane aeroplane photography |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110986888A (en) * | 2019-12-17 | 2020-04-10 | 中煤航测遥感集团有限公司 | Aerial photography integrated method |
| CN113496461A (en) * | 2020-03-18 | 2021-10-12 | 广州极飞科技股份有限公司 | Point cloud data processing method and device, computer equipment and storage medium |
| CN111678502A (en) * | 2020-06-09 | 2020-09-18 | 中国科学院东北地理与农业生态研究所 | Method for extracting frozen soil disaster information based on unmanned aerial vehicle aerial survey image |
| CN111678502B (en) * | 2020-06-09 | 2022-06-14 | 中国科学院东北地理与农业生态研究所 | Method for extracting frozen soil disaster information based on unmanned aerial vehicle aerial survey image |
| CN111707620A (en) * | 2020-06-11 | 2020-09-25 | 中国电建集团华东勘测设计研究院有限公司 | Classification rule set for land utilization and water and soil loss monitoring method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110440761B (en) | 2022-05-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110440761A (en) | A kind of processing method of unmanned plane aerophotogrammetry data | |
| CN111062915B (en) | Real-time steel pipe defect detection method based on improved YOLOv3 model | |
| CN107830846B (en) | Method for measuring angle of communication tower antenna by using unmanned aerial vehicle and convolutional neural network | |
| CN111815717B (en) | Multi-sensor fusion external parameter combination semi-autonomous calibration method | |
| CN110826549A (en) | Inspection robot instrument image identification method and system based on computer vision | |
| CN107044821A (en) | A kind of measuring method and system of contactless tubing object | |
| CN105447868A (en) | Automatic checking method for aerial data of mini unmanned air vehicle | |
| US11769225B2 (en) | Image processing apparatus, image processing method, and program | |
| CN113643254B (en) | Efficient collection and processing method for laser point cloud of unmanned aerial vehicle | |
| CN109829923B (en) | Base station antenna downward inclination angle measurement system and method based on deep neural network | |
| CN114663787A (en) | Single-tree segmentation method fusing unmanned aerial vehicle CHM and RGB images | |
| CN111340942A (en) | Three-dimensional reconstruction system based on unmanned aerial vehicle and method thereof | |
| Motayyeb et al. | Fusion of UAV-based infrared and visible images for thermal leakage map generation of building facades | |
| CN111598937A (en) | Farmland land area measurement method and system based on calibration block target correction | |
| CN114998769A (en) | CBAM (cubic boron nitride) and improved YOLOX (YOLOX) -based rapid detection method and device for small aerial object of unmanned aerial vehicle | |
| CN114692991A (en) | Wolfberry yield prediction method and system based on deep learning | |
| CN110618696B (en) | Air-ground integrated surveying and mapping unmanned aerial vehicle | |
| CN109461137B (en) | Object-oriented orthographic image quality inspection method based on gray level correlation | |
| CN114169404A (en) | Method for intelligently acquiring quantitative information of slope diseases based on images | |
| Wang et al. | Fast stitching of DOM based on small UAV | |
| CN116596508B (en) | Slope crack disease development identification recording method | |
| CN114782801B (en) | Machine learning-based automatic control point layout method and device | |
| CN117670979B (en) | Bulk cargo volume measurement method based on fixed point position monocular camera | |
| CN112800929B (en) | Bamboo shoot quantity and high growth rate online monitoring method based on deep learning | |
| CN103884631A (en) | Quick testing method for particle sizes of aromatic beads for tobacco |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |