CN106871864A - A kind of method that depth of building is automatically extracted based on three-dimensional satellite image - Google Patents
A kind of method that depth of building is automatically extracted based on three-dimensional satellite image Download PDFInfo
- Publication number
- CN106871864A CN106871864A CN201710076323.2A CN201710076323A CN106871864A CN 106871864 A CN106871864 A CN 106871864A CN 201710076323 A CN201710076323 A CN 201710076323A CN 106871864 A CN106871864 A CN 106871864A
- Authority
- CN
- China
- Prior art keywords
- data
- target structures
- structures thing
- building
- value
- 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
- G01C5/00—Measuring height; Measuring distances transverse to line of sight; Levelling between separated points; Surveyors' levels
-
- 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
- G01C11/02—Picture taking arrangements specially adapted for photogrammetry or photographic surveying, e.g. controlling overlapping of pictures
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Multimedia (AREA)
- Image Processing (AREA)
- Processing Or Creating Images (AREA)
Abstract
The invention discloses a kind of method that depth of building is automatically extracted based on three-dimensional satellite image, it is related to satellite image to interpret field.Methods described includes：Obtain original satellite stereogram, SRTM data and the DOM data of target structures thing；Original satellite stereogram is pre-processed, after then carrying out relative orientation and absolute orientation successively, the core line image for extracting DSM data is generated；The initial DSM data of target structures thing is extracted, with reference to the DOM data of DLG data roads figure layer, landforms figure layer and target structures thing, satisfactory checkpoint is obtained；Acquisition filters the dem data of target structures thing elevation information；Integral data；Target structures thing summit height value, base elevation value carry out stereoscopic examination and amendment；With reference to the house figure layer of target structures thing in DOM, DLG, building summit height value and building place base height value are subtracted each other into acquisition building elevation information.The present invention makes extraction building elevation information more rapidly and efficiently.
Description
Technical field
Building is automatically extracted the present invention relates to satellite image interpretation field, more particularly to a kind of solid satellite image that is based on
The method of height.
Background technology
Since 20th century, remote sensing technology is reached its maturity, and low flyer is developed rapidly.Carried as the image for being formed is relied on
The technology of taking reaches its maturity, meanwhile, with the application of 3-D technology, extract building elevation information important role.Existing use
Include laser range finder, stereogram, optical image altimetry and shade range finding etc. in the extractive technique of building elevation information
Various methods, but, these methods all Shortcomings：
Laser range finder, because human factor influences larger, causes error unbalanced, to the verification workload of gathered data
Greatly., it is necessary to technical professional, collecting efficiency is low, and labor intensive is larger, high cost for stereogram.Optical image altimetry,
Need the positions such as angle point of building in artificial collection image, due to being blocked by building, photocopy, sun altitude etc. it is multi-party
Face rings, and gathered data precision is relatively low, meanwhile, because using artificial collection point position, production efficiency is relatively low.
The content of the invention
It is an object of the invention to provide a kind of method that depth of building is automatically extracted based on three-dimensional satellite image, so that
Solve foregoing problems present in prior art.
To achieve these goals, the method that depth of building is automatically extracted based on three-dimensional satellite image of the present invention,
Methods described includes：
S1, obtains original satellite stereogram, SRTM data and the DOM data of target structures thing；By the SRTM data
With the DOM data as absolute orientation orientation point data source；Wherein, the SRTM data are controlled as orientation point height
Data source, the DOM data as orientation point plane control data source；
S2, the original satellite stereogram to target structures thing is pre-processed, to pretreated picture to carrying out successively
Relative orientation and absolute orientation, judge whether stereogram plane error and vertical error reach default threshold after absolute orientation
Value, if it is, generating for extracting the core line image of DSM data, and enters S3；If it is not, then repeating S2；
S3, the core line image to obtaining carries out matching treatment, extracts the initial DSM data of the target structures thing,
With reference to the DOM data of DLG data roads figure layer, landforms figure layer and target structures thing, selection checkpoint is uniformly distributed in a triangle
Position, extracts height value of the checkpoint position in SRTM and DSM, and try to achieve each checkpoint position checkpoint residual values, check-up through statistical means respectively
Point residual values calculate mean square error of height, judge whether checkpoint mean square error of height meets the threshold value for pre-setting, if it is,
Into S4；If it is not, then returning to S2, pretreatment is re-started, untill into S4；
S4, the width and the gradient of the target structures thing with reference to described in DLG data set filtering process parameter, to initial DSM
Data are filtered treatment, the stereogram that filtered DSM data is imported, and then check the target by stereoscopic device
Whether the filtered DSM data of building meets default examination scope, if met, obtains removal target structures thing elevation letter
The dem data of breath；If do not met, filtering parameter is reset, reenter filtering process and judge, until being filtered
Untill the dem data of target structures thing elevation information；
S5, initial DSM data is incorporated into DLG data with the dem data for filtering target structures thing elevation information, will be whole
Target structures thing figure layer data in DLG data after conjunction carry out classification treatment according to the attribute in house, by target structures thing figure
After layer data carries out dividing processing according to building structure, the house figure layer of target structures thing is obtained and stored；Finally automatically extract
Target structures thing summit height value and base elevation value；
S6, stereoscopic examination is carried out by target structures thing summit height value, base elevation value, judges summit height value, ground
Whether height value is accurate, if it is, the number of passes high of target structures thing summit height value, base elevation value for target structures thing
According to；If it is not, then exist abnormal elevation carry out three-dimensional modification, in step s 2 to stereogram in gather correct summit
Height value and base elevation value, and the height value that will be collected is used as the altitude data of target structures thing；
S7, with reference to the house figure layer of target structures thing in DOM, DLG, by building summit height value and building place base
Height value subtracts each other acquisition building elevation information.
Preferably, in step S2, the pretreatment is that the original satellite stereogram is converted into 8 tif form shadows
Picture, and enhancing treatment is carried out to tif format images, the enhancing treatment includes building edge treated, image contrast treatment, line
Reason treatment and ratio proccessing.
Preferably, in step S2, the predetermined threshold value be precision residual error more than 1 pixel and no more than two times in error.
Preferably, in step S4, the dem data for obtaining removal target structures thing elevation information, specifically according to following
Step is realized：Examination scope is determined according to water system key element figure layer in DL G datas, by the target structures in examination scope
The filtered DSM data of thing and SRTM Data Integrations, reject the noise matched with water system key element figure layer in DSM data, are gone
Except the dem data of target structures thing elevation information.
The acquisition of depth of building is automatic based on DLG aggregation of data analysis DSM, DEM and SRTM data in the present invention
Building roof and base elevation are extracted, building summit elevation will be obtained and base elevation is imported stereogram inspection, to depositing
Mistake carry out three-dimensional amendment, finally derive correct building summit elevation and base elevation, will obtain building summit
Elevation and base elevation subtract each other the height value for obtaining building.
The beneficial effects of the invention are as follows：
The present invention provides a kind of acquisition methods of building elevation information, and the present invention makes extraction building elevation information more
Rapidly and efficiently, the solution of high-efficiency and economic is provided to extract depth of building information on a large scale.
Brief description of the drawings
Fig. 1 is the schematic flow sheet of the method that depth of building is automatically extracted based on three-dimensional satellite image.
Specific embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, below in conjunction with accompanying drawing, the present invention is entered
Row is further described.It should be appreciated that specific embodiment described herein is only used to explain the present invention, it is not used to
Limit the present invention.
Implement Core flow of the invention is：By building figure layer in DLG, specify and obtain elevation information geographical position seat
Mark, house earth surface point height value is obtained according to the dem data after treatment, and house summit height value is obtained according to DSM data, and right
Obtaining result carries out three-dimensional judgement, and acquisition building elevation information is calculated by obtaining result.
Embodiment
The method for automatically extracting depth of building based on three-dimensional satellite image described in the present embodiment, methods described includes：
S1, obtains original satellite stereogram, SRTM data and the DOM data of target structures thing；By the SRTM data
With the DOM data as absolute orientation orientation point data source；Wherein, the SRTM data are controlled as orientation point height
Data source, the DOM data as orientation point plane control data source；
S2, the original satellite stereogram to target structures thing is pre-processed, to pretreated picture to carrying out successively
Relative orientation and absolute orientation, judge whether stereogram plane error and vertical error reach default threshold after absolute orientation
Value, if it is, generating for extracting the core line image of DSM data, and enters S3；If it is not, then repeating S2；
S3, the core line image to obtaining carries out matching treatment, extracts the initial DSM data of the target structures thing,
With reference to the DOM data of DLG data roads figure layer, landforms figure layer and target structures thing, selection checkpoint is uniformly distributed in a triangle
Position, extracts height value of the checkpoint position in SRTM and DSM, and try to achieve each checkpoint position checkpoint residual values, check-up through statistical means respectively
Point residual values calculate mean square error of height, judge whether checkpoint mean square error of height meets the threshold value for pre-setting, if it is,
Into S4；If it is not, then returning to S2, pretreatment is re-started, untill into S4；
S4, the width and the gradient of the target structures thing with reference to described in DLG data set filtering process parameter, to initial DSM
Data are filtered treatment, the stereogram that filtered DSM data is imported, and then check the target by stereoscopic device
Whether the filtered DSM data of building meets default examination scope, if met, obtains removal target structures thing elevation letter
The dem data of breath；If do not met, filtering parameter is reset, reenter filtering process and judge, until being filtered
Untill the dem data of target structures thing elevation information；
S5, initial DSM data is incorporated into DLG data with the dem data for filtering target structures thing elevation information, will be whole
Target structures thing figure layer data in DLG data after conjunction carry out classification treatment according to the attribute in house, by target structures thing figure
After layer data carries out dividing processing according to building structure, the house figure layer of target structures thing is obtained and stored；Finally automatically extract
Target structures thing summit height value and base elevation value；
S6, stereoscopic examination is carried out by target structures thing summit height value, base elevation value, judges summit height value, ground
Whether height value is accurate, if it is, the number of passes high of target structures thing summit height value, base elevation value for target structures thing
According to；If it is not, then exist abnormal elevation carry out three-dimensional modification, in step s 2 to stereogram in gather correct summit
Height value and base elevation value, and the height value that will be collected is used as the altitude data of target structures thing；
S7, with reference to the house figure layer of target structures thing in DOM, DLG, by building summit height value and building place base
Height value subtracts each other acquisition building elevation information.Explanation is explained in more detail is：
(1) in step S2, relative orientation is to recover or determine that two light beams of stereogram are closed in photography moment picture to position
The process of system.Absolute orientation is to determine the operation process of stereogram residing orientation and ratio in object coordinates system.Carry out absolutely
, it is necessary to carry out field operation control measurement or using available data as using orientation point in absolute orientation during to orientation, wherein, orientation point
Comprising x, tri- dimensions of y, z, usual x and y is plane, and z is elevation, in the present embodiment by target structures thing in step S1
DOM data and SRTM data are used as the orientation point data source for carrying out absolute orientation.
(2) in step S2, the pretreatment is that the original satellite stereogram is converted into 8 tif format images,
And enhancing treatment is carried out to tif format images, the enhancing treatment includes building edge treated, image contrast treatment, texture
Treatment and ratio proccessing.
The predetermined threshold value be precision residual error more than 1 pixel and no more than two times in error.
In the step S2, to pretreated picture to carrying out the operation after relative orientation and absolute orientation and thereafter respectively
Operation is specially in step S2：To pretreated picture to carrying out relative orientation and absolute orientation respectively after, respectively obtain relative
The picture pair after the post processing of picture pair and absolute orientation after directional process；Judge relative orientation treatment after picture pair precision and definitely
Whether the precision of the picture pair after orientation post processing reaches the threshold value for pre-setting, if it is, generation target structures thing is vertical
Body image pair, and enter S3；If the picture after any one directional process to precision to reach threshold value set in advance, then to mesh
The original satellite space image for marking building re-starts pretreatment and directional process.Directional process includes that relative orientation is processed and exhausted
To directional process.
(3) in step S3, DLG data by from storage in the initial data of target structures thing, or by initial data
In DOM data interpretations obtain.
Because building, trees can caused by match initial DSM data fall on top of building or trees, be not ground high
Journey, so, it is necessary to reference to road figure layer in DLG data, the regions such as road surface, vacant lot are distributed in a triangle.
(4) in step S4, the dem data for obtaining removal target structures thing elevation information, specifically as steps described below
Realize：Examination scope is determined according to water system key element figure layer in DL G datas, by the target structures thing filter in examination scope
DSM data and SRTM Data Integrations after ripple, reject the noise matched with water system key element figure layer in DSM data, obtain removing mesh
Mark the dem data of building elevation information.
By using above-mentioned technical proposal disclosed by the invention, following beneficial effect has been obtained：
The present invention provides a kind of acquisition methods of building elevation information, and the present invention makes extraction building elevation information more
Rapidly and efficiently, the solution of high-efficiency and economic is provided to extract depth of building information on a large scale.
The above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, under the premise without departing from the principles of the invention, some improvements and modifications can also be made, these improvements and modifications also should
Depending on protection scope of the present invention.
Claims (4)
1. a kind of method that depth of building is automatically extracted based on three-dimensional satellite image, it is characterised in that methods described includes：
S1, obtains original satellite stereogram, SRTM data and the DOM data of target structures thing；By the SRTM data and institute
State orientation point data source of the DOM data as absolute orientation；Wherein, the data that the SRTM data are controlled as orientation point height
Source, the DOM data as orientation point plane control data source；
S2, the original satellite stereogram to target structures thing is pre-processed, relative to carrying out successively to pretreated picture
Orientation and absolute orientation, judge whether stereogram plane error and vertical error reach predetermined threshold value after absolute orientation, such as
Fruit is then generated for extracting the core line image of DSM data, and enters S3；If it is not, then repeating S2；
S3, the core line image to obtaining carries out matching treatment, extracts the initial DSM data of the target structures thing, reference
The DOM data of DLG data roads figure layer, landforms figure layer and target structures thing, are uniformly distributed selection checkpoint position in a triangle, point
Height value of the checkpoint position in SRTM and DSM is indescribably taken, and tries to achieve each checkpoint position checkpoint residual values, check-up through statistical means point residual error
Value calculates mean square error of height, judges whether checkpoint mean square error of height meets the threshold value for pre-setting, if it is, into
S4；If it is not, then returning to S2, pretreatment is re-started, untill into S4；
S4, the width and the gradient of the target structures thing with reference to described in DLG data set filtering process parameter, to initial DSM data
Treatment is filtered, then the stereogram that filtered DSM data is imported checks the target structures by stereoscopic device
Whether the filtered DSM data of thing meets default examination scope, if met, obtains removal target structures thing elevation information
Dem data；If do not met, filtering parameter is reset, reenter filtering process and judge, until acquisition filters target
Untill the dem data of building elevation information；
S5, initial DSM data is incorporated into DLG data with the dem data for filtering target structures thing elevation information, after integration
DLG data in target structures thing figure layer data carry out classification treatment according to the attribute in house, by target structures thing figure layer number
After dividing processing is carried out according to building structure, the house figure layer of target structures thing is obtained and stored；Finally automatically extract target
Building summit height value and base elevation value；
S6, stereoscopic examination is carried out by target structures thing summit height value, base elevation value, judges summit height value, base elevation
Whether value is accurate, if it is, the altitude data of target structures thing summit height value, base elevation value for target structures thing；
If it is not, then the abnormal elevation for existing carries out three-dimensional modification, in step s 2 to stereogram in gather correct summit high
Journey value and base elevation value, and the height value that will be collected is used as the altitude data of target structures thing；
S7, with reference to the house figure layer of target structures thing in DOM, DLG, by building summit height value and building place base elevation
Value subtracts each other acquisition building elevation information.
2. method according to claim 1, it is characterised in that in step S2, the pretreatment is to found the original satellite
Body image carries out enhancing treatment to tif format images to being converted to 8 tif format images, and the enhancing treatment includes building
Edge treated, image contrast treatment, texture processing and ratio proccessing.
3. method according to claim 1, it is characterised in that in step S2, the predetermined threshold value is that precision residual error is more than 1
Pixel and no more than two times in error.
4. method according to claim 1, it is characterised in that described to obtain removal target structures thing elevation letter in step S4
The dem data of breath, specifically realizes as steps described below：Determine examination scope according to water system key element figure layer in DL G datas, will be
The filtered DSM data of target structures thing and SRTM Data Integrations in examination scope, will with water system in rejecting DSM data
The noise of sketch map layer matching, obtains removing the dem data of target structures thing elevation information.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710076323.2A CN106871864B (en) | 2017-02-13 | 2017-02-13 | A method of depth of building is automatically extracted based on three-dimensional satellite image |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710076323.2A CN106871864B (en) | 2017-02-13 | 2017-02-13 | A method of depth of building is automatically extracted based on three-dimensional satellite image |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106871864A true CN106871864A (en) | 2017-06-20 |
| CN106871864B CN106871864B (en) | 2019-03-01 |
Family
ID=59166074
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710076323.2A Active CN106871864B (en) | 2017-02-13 | 2017-02-13 | A method of depth of building is automatically extracted based on three-dimensional satellite image |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106871864B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107798731A (en) * | 2017-11-10 | 2018-03-13 | 泰瑞数创科技(北京)有限公司 | A kind of method based on satellite image automatic modeling |
| CN109443359A (en) * | 2018-09-27 | 2019-03-08 | 北京空间机电研究所 | A kind of geographic positioning of ground full-view image |
| CN111121650A (en) * | 2019-12-25 | 2020-05-08 | 中国民航科学技术研究院 | Method and system for measuring and calculating building height by using DEM (digital elevation model) to assist satellite images |
| CN111465971A (en) * | 2017-12-08 | 2020-07-28 | 亚洲航测株式会社 | Device for generating image for highly coloring ground object foundation and program for generating image for highly coloring ground object foundation |
| CN111709432A (en) * | 2020-06-19 | 2020-09-25 | 天津市滨海新区水务局 | InSAR ground point extraction method, device, server and storage medium in complex urban environment |
| CN114417489A (en) * | 2022-03-30 | 2022-04-29 | 宝略科技(浙江)有限公司 | Building base contour refinement extraction method based on real-scene three-dimensional model |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101545775A (en) * | 2009-05-05 | 2009-09-30 | 东南大学 | Method for calculating orientation elements of photo and the height of building by utilizing digital map |
| CN102855810A (en) * | 2012-09-04 | 2013-01-02 | 绍兴文理学院 | Digital line graphic method based on satellite image map |
| CN202794518U (en) * | 2012-08-27 | 2013-03-13 | 西安煤航信息产业有限公司 | Accuracy detecting system of 3-dimensional (3D) product produced based on onboard interferometric synthetic aperture radar (INSAR) |
| JP2013054660A (en) * | 2011-09-06 | 2013-03-21 | Asia Air Survey Co Ltd | Vegetation map creation device and vegetation map creation program |
| CN103115614A (en) * | 2013-01-21 | 2013-05-22 | 武汉大学 | Associated parallel matching method for multi-source multi-track long-strip satellite remote sensing images |
| CN103884321A (en) * | 2014-04-17 | 2014-06-25 | 西安煤航信息产业有限公司 | Remote-sensing image mapping process |
| CN104457691A (en) * | 2014-12-15 | 2015-03-25 | 重庆市勘测院 | Method for obtaining elevation information of main building body |
| CN104637370A (en) * | 2014-12-23 | 2015-05-20 | 河南城建学院 | Photogrammetry and remote sensing comprehensive teaching method and system |
| CN104729529A (en) * | 2013-12-24 | 2015-06-24 | 北京市测绘设计研究院 | Method and system for judging errors of topographic map surveying system |
| CN204832512U (en) * | 2015-09-14 | 2015-12-02 | 西安煤航信息产业有限公司 | DLG data acquisition system based on liDAR point cloud data and image |
| JP2016085145A (en) * | 2014-10-28 | 2016-05-19 | 公益財団法人鉄道総合技術研究所 | Ground surface change estimation device and ground surface change estimation program |
| CN105719341A (en) * | 2016-01-18 | 2016-06-29 | 中科宇图科技股份有限公司 | Method for extracting building height from space remote-sensing image based on RPC model |
| WO2016131847A1 (en) * | 2015-02-20 | 2016-08-25 | Prox Dynamics As | Method for calculating the distance to a ground target from an aerial vehicle |
| CN106294001A (en) * | 2016-07-25 | 2017-01-04 | 中国科学院国家天文台 | A kind of restorative procedure cheating graphic data singularly for moon annular |
-
2017
- 2017-02-13 CN CN201710076323.2A patent/CN106871864B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101545775A (en) * | 2009-05-05 | 2009-09-30 | 东南大学 | Method for calculating orientation elements of photo and the height of building by utilizing digital map |
| JP2013054660A (en) * | 2011-09-06 | 2013-03-21 | Asia Air Survey Co Ltd | Vegetation map creation device and vegetation map creation program |
| CN202794518U (en) * | 2012-08-27 | 2013-03-13 | 西安煤航信息产业有限公司 | Accuracy detecting system of 3-dimensional (3D) product produced based on onboard interferometric synthetic aperture radar (INSAR) |
| CN102855810A (en) * | 2012-09-04 | 2013-01-02 | 绍兴文理学院 | Digital line graphic method based on satellite image map |
| CN103115614A (en) * | 2013-01-21 | 2013-05-22 | 武汉大学 | Associated parallel matching method for multi-source multi-track long-strip satellite remote sensing images |
| CN104729529A (en) * | 2013-12-24 | 2015-06-24 | 北京市测绘设计研究院 | Method and system for judging errors of topographic map surveying system |
| CN103884321A (en) * | 2014-04-17 | 2014-06-25 | 西安煤航信息产业有限公司 | Remote-sensing image mapping process |
| JP2016085145A (en) * | 2014-10-28 | 2016-05-19 | 公益財団法人鉄道総合技術研究所 | Ground surface change estimation device and ground surface change estimation program |
| CN104457691A (en) * | 2014-12-15 | 2015-03-25 | 重庆市勘测院 | Method for obtaining elevation information of main building body |
| CN104637370A (en) * | 2014-12-23 | 2015-05-20 | 河南城建学院 | Photogrammetry and remote sensing comprehensive teaching method and system |
| WO2016131847A1 (en) * | 2015-02-20 | 2016-08-25 | Prox Dynamics As | Method for calculating the distance to a ground target from an aerial vehicle |
| CN204832512U (en) * | 2015-09-14 | 2015-12-02 | 西安煤航信息产业有限公司 | DLG data acquisition system based on liDAR point cloud data and image |
| CN105719341A (en) * | 2016-01-18 | 2016-06-29 | 中科宇图科技股份有限公司 | Method for extracting building height from space remote-sensing image based on RPC model |
| CN106294001A (en) * | 2016-07-25 | 2017-01-04 | 中国科学院国家天文台 | A kind of restorative procedure cheating graphic data singularly for moon annular |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107798731A (en) * | 2017-11-10 | 2018-03-13 | 泰瑞数创科技(北京)有限公司 | A kind of method based on satellite image automatic modeling |
| CN111465971A (en) * | 2017-12-08 | 2020-07-28 | 亚洲航测株式会社 | Device for generating image for highly coloring ground object foundation and program for generating image for highly coloring ground object foundation |
| CN111465971B (en) * | 2017-12-08 | 2022-03-22 | 亚洲航测株式会社 | Device and method for generating image for highly coloring ground object foundation |
| CN109443359A (en) * | 2018-09-27 | 2019-03-08 | 北京空间机电研究所 | A kind of geographic positioning of ground full-view image |
| CN111121650A (en) * | 2019-12-25 | 2020-05-08 | 中国民航科学技术研究院 | Method and system for measuring and calculating building height by using DEM (digital elevation model) to assist satellite images |
| CN111121650B (en) * | 2019-12-25 | 2021-08-31 | 中国民航科学技术研究院 | Method and system for measuring and calculating building height by using DEM (digital elevation model) to assist satellite images |
| CN111709432A (en) * | 2020-06-19 | 2020-09-25 | 天津市滨海新区水务局 | InSAR ground point extraction method, device, server and storage medium in complex urban environment |
| CN111709432B (en) * | 2020-06-19 | 2023-04-25 | 天津市滨海新区水务局 | InSAR ground point extraction method, device, server and storage medium in complex urban environment |
| CN114417489A (en) * | 2022-03-30 | 2022-04-29 | 宝略科技(浙江)有限公司 | Building base contour refinement extraction method based on real-scene three-dimensional model |
| CN114417489B (en) * | 2022-03-30 | 2022-07-19 | 宝略科技(浙江)有限公司 | Building base contour refinement extraction method based on real-scene three-dimensional model |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106871864B (en) | 2019-03-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106871864A (en) | A kind of method that depth of building is automatically extracted based on three-dimensional satellite image | |
| CN108020211B (en) | Method for estimating biomass of invasive plants through aerial photography by unmanned aerial vehicle | |
| Piermattei et al. | Suitability of ground-based SfM–MVS for monitoring glacial and periglacial processes | |
| CN102609726B (en) | Method for classifying remote sensing images blended with high-space high-temporal-resolution data by object oriented technology | |
| Rau et al. | Analysis of oblique aerial images for land cover and point cloud classification in an urban environment | |
| Turker et al. | Detection of collapsed buildings caused by the 1999 Izmit, Turkey earthquake through digital analysis of post-event aerial photographs | |
| US20170140578A1 (en) | Depth camera-based human-body model acquisition method and network virtual fitting system | |
| CN106529469A (en) | Unmanned aerial vehicle airborne LiDAR point cloud filtering method based on adaptive gradient | |
| CN102622738B (en) | Method for recovering spectral information of hill shade area of Landsat thematic mapper/enhanced thematic mapper plus (TM/ETM+) image | |
| TW201022708A (en) | Method of change detection for building models | |
| CN105989322A (en) | High-resolution remote sensing image-based multi-index fusion landslide detection method | |
| Kim et al. | Semiautomatic reconstruction of building height and footprints from single satellite images | |
| CN105547287B (en) | A kind of irregular coelonavigation sight information extracting method | |
| CN108195736B (en) | Method for extracting vegetation canopy clearance rate through three-dimensional laser point cloud | |
| CN107818303A (en) | Unmanned plane oil-gas pipeline image automatic comparative analysis method, system and software memory | |
| CN109948471A (en) | Based on the traffic haze visibility detecting method for improving InceptionV4 network | |
| CN113033390A (en) | Dam remote sensing intelligent detection method based on deep learning | |
| Berveglieri et al. | Identification of successional stages and cover changes of tropical forest based on digital surface model analysis | |
| CN106969753B (en) | Unmanned plane data processing method based on Electric Design application | |
| CN114549956A (en) | Deep learning assisted inclined model building facade target recognition method | |
| CN109584250A (en) | A kind of method that the visual zone of robust divides mark automatically | |
| Anders et al. | Rule set transferability for object-based feature extraction: An example for cirque mapping | |
| CN107749080A (en) | A kind of DEM immediate processing methods based on cloud data | |
| CN113052110B (en) | Three-dimensional interest point extraction method based on multi-view projection and deep learning | |
| Luo et al. | An evolutionary shadow correction network and a benchmark UAV dataset for remote sensing images |
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 | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right |
Denomination of invention: A method of automatically extracting building height based on stereo satellite image Effective date of registration: 20211209 Granted publication date: 20190301 Pledgee: Haidian Beijing science and technology enterprise financing Company limited by guarantee Pledgor: CHINA SCIENCE MAPUNIVERSE TCHNDOGY Co.,Ltd. Registration number: Y2021110000090 |