CN108648275A - Urban changes solid Remote Sensing Images Matching suspicious region Automated inspection method - Google Patents
Urban changes solid Remote Sensing Images Matching suspicious region Automated inspection method Download PDFInfo
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- CN108648275A CN108648275A CN201810441425.4A CN201810441425A CN108648275A CN 108648275 A CN108648275 A CN 108648275A CN 201810441425 A CN201810441425 A CN 201810441425A CN 108648275 A CN108648275 A CN 108648275A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/05—Geographic models
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2200/00—Indexing scheme for image data processing or generation, in general
- G06T2200/08—Indexing scheme for image data processing or generation, in general involving all processing steps from image acquisition to 3D model generation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
- G06T2207/10012—Stereo images
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30181—Earth observation
Abstract
The invention discloses urban changes solid Remote Sensing Images Matching suspicious region Automated inspection methods, using remote sensing monitoring, automatic monitoring and the artificial three kinds of approach of survey that patrol obtain and the relevant monitoring index data of city suspicious region, including asd number and positioning index, air quality indexes, temperature index, passerby's quantitative index and video image, the data of acquisition pass through the Internet transmission to data center.The present invention by remote sensing monitoring, automatic monitoring and it is artificial patrol survey it is hidden to city can coagulation zone domain be difficult to be monitored, can not achieve to understanding concrete condition, be monitoring blind area, it is simple and convenient, and by data analytical precision higher.
Description
Technical field
The invention belongs to field of measuring technique more particularly to urban changes solid Remote Sensing Images Matching suspicious region to automate
The method of inspection.
Background technology
In the foundation and application process of three-dimensional city simulation model, need to be related to and utilize a large amount of data.In recent years
Come, remote sensing technology is grown rapidly, and remote sensing image resolution ratio is higher and higher, can reach even tens centimetres of 1~2m.These are high
The remote sensing image of resolution ratio provides advantage for the modeling of three-dimensional city simulation model and data update, and people believe remote sensing
The understanding and producing level of breath will far behind obtain the speed of information source by space and air line, and there are no one so far
The system of set perfect in shape and function can be realized automatically extracts road, building etc. using computer linearly from air remote sensing digitized video
Man-made features information, at the same time, with the continuous development of computer hardware technique, large-scale and complex scenes are built in real time
Mould has become possibility, this proposes new requirement to the complexity and the sense of reality of model, but builds and slightly show complicated three-dimensional mould
Type remains the work of a unusual time and effort consuming.
Patent No. CN201711304676.X, applying date 2017-12-11 disclose a kind of accurate wisdom of planning
Urban Planning System, which is characterized in that described including information subsystem, project subsystem, planning subsystem and display terminal
Information subsystem is for obtaining urban remote sensing image, and the project subsystem is for obtaining City Construction Project, planning
System is for planning city according to urban remote sensing image and City Construction Project, and the display terminal is for showing city
Plan situation in city.
Above-mentioned patent is laid a good foundation by realizing the accurate planning of smart city for urban construction.But in city
It is hidden can coagulation zone domain be difficult to be monitored, can not achieve to understand concrete condition, be monitoring blind area.
Invention content
The purpose of the present invention is exactly to overcome the problems of the above-mentioned prior art, urban changes solid remote sensing image
It, can be difficult to monitoring the hidden suspicious region in city with suspicious region Automated inspection method.
In order to achieve the above-mentioned object of the invention, technical scheme is as follows：
Urban changes solid Remote Sensing Images Matching suspicious region Automated inspection method, it is characterised in that：Include the following steps：
A. remote sensing monitoring, automatic monitoring were used and manually patrols three kinds of approach of survey and obtains and was referred to the relevant monitoring of city suspicious region
Data, including asd number and positioning index, air quality indexes, temperature index, passerby's quantitative index and video image are marked, are obtained
The data taken pass through the Internet transmission to data center；
B. backup and data processing are carried out to the data of data center；
C. Three-dimension Numerical Model is built according to the data source stored in database, including is particularly shown building situation, air matter
Amount, temperature data, number synthesize a solid figure.
D. edge extracting and refinement based on the left image of stereogram obtain the bianry image of Single pixel edge；
E. bianry image is controlled and is measured：The multiple picture control points laid in measured region are measured respectively, and
Obtain control survey of aerial photograph data；
F. dem data acquisition and dem data collection editor are carried out to measurement data, just completes the DEM digital elevations of measured region
The manufacturing process of model.
Bianry image in the step D includes the following steps：
A. bianry image I O are extended for the square-shaped image that the length of side is equal to 4 N, extending method is to increase 4N- in the lower section of I O
H rows, all logical zeros of pixel value in increased row, i.e. black color dots；Then the image after having carried out above-mentioned row and having expanded again
Right increase 6N-W row, the also all logical zeros of the pixel value in increased row；
B., I is converted to the coded sequence L based on quaternary tree；
C. a series of basic logic operations that the bianry image logical operation carried out will be needed to be converted to bianry images, that is, have two
The basic dyadic logical operation of a operand, including logic and operation AND, logic or operation OR, logic XOR operation XOR and
Logic difference operation SUB, and the basic monadic logic operation with single operation number, i.e. logic inverse operation NOT；For two-value
Image I, logical not operation NOT's the result is that a width and I same sizes bianry image I R, i.e. I R=NOTI.
Dem data obtains in the step F and editor includes the following steps：
A. the dem data of extraction zoning and closed area boundary, and calculate intersecting point coordinate；
B. by intersection point data prediction, judge putting in order for intersection point, calculate the ranks number where intersection point；
C. intersection point puts in order, and most northern in zoning, most eastern, most southern, most Western-style pastry coordinate counts this four points in intersection point
Then arrangement position in file judges putting in order for intersection point according to arrangement position；
D. calculated according to the effective area for carrying out borderline region that puts in order, by section successively based on into edlin.
Beneficial effects of the present invention：
（One）The present invention by remote sensing monitoring, automatic monitoring and it is artificial patrol survey it is hidden to city can coagulation zone domain be difficult to be monitored,
It can not achieve to understanding concrete condition, be monitoring blind area, it is simple and convenient, and by data analytical precision higher.
（Two）The present invention improves measurement accuracy, the threedimensional model image of generation by the calculation of bianry image numerical value
It is more true to nature.
（Three）The influence that the present invention was obtained and edited can be quickly by life entity to topographic map by dem data is eliminated,
So as to obtain more accurate topographic map.
Specific implementation mode
The present invention is described in further detail with reference to embodiment, embodiments of the present invention are not limited thereto.
Embodiment 1
Urban changes solid Remote Sensing Images Matching suspicious region Automated inspection method, it is characterised in that：Include the following steps：
A. remote sensing monitoring, automatic monitoring were used and manually patrols three kinds of approach of survey and obtains and was referred to the relevant monitoring of city suspicious region
Data, including asd number and positioning index, air quality indexes, temperature index, passerby's quantitative index and video image are marked, are obtained
The data taken pass through the Internet transmission to data center；
B. backup and data processing are carried out to the data of data center；
C. Three-dimension Numerical Model is built according to the data source stored in database, including is particularly shown building situation, air matter
Amount, temperature data, number synthesize a solid figure.
D. edge extracting and refinement based on the left image of stereogram obtain the bianry image of Single pixel edge；
E. bianry image is controlled and is measured：The multiple picture control points laid in measured region are measured respectively, and
Obtain control survey of aerial photograph data；
F. dem data acquisition and dem data collection editor are carried out to measurement data, just completes the DEM digital elevations of measured region
The manufacturing process of model.
The present invention by remote sensing monitoring, automatic monitoring and it is artificial patrol survey it is hidden to city can coagulation zone domain be difficult to be monitored,
It can not achieve to understanding concrete condition, be monitoring blind area, it is simple and convenient, and by data analytical precision higher.
Embodiment 2
Urban changes solid Remote Sensing Images Matching suspicious region Automated inspection method, it is characterised in that：Include the following steps：
A. remote sensing monitoring, automatic monitoring were used and manually patrols three kinds of approach of survey and obtains and was referred to the relevant monitoring of city suspicious region
Data, including asd number and positioning index, air quality indexes, temperature index, passerby's quantitative index and video image are marked, are obtained
The data taken pass through the Internet transmission to data center；
B. backup and data processing are carried out to the data of data center；
C. Three-dimension Numerical Model is built according to the data source stored in database, including is particularly shown building situation, air matter
Amount, temperature data, number synthesize a solid figure.
D. edge extracting and refinement based on the left image of stereogram obtain the bianry image of Single pixel edge；
E. bianry image is controlled and is measured：The multiple picture control points laid in measured region are measured respectively, and
Obtain control survey of aerial photograph data；
F. dem data acquisition and dem data collection editor are carried out to measurement data, just completes the DEM digital elevations of measured region
The manufacturing process of model.
Bianry image in the step D includes the following steps：
A. bianry image I O are extended for the square-shaped image that the length of side is equal to 4 N, extending method is to increase 4N- in the lower section of I O
H rows, all logical zeros of pixel value in increased row, i.e. black color dots；Then the image after having carried out above-mentioned row and having expanded again
Right increase 6N-W row, the also all logical zeros of the pixel value in increased row；
B., I is converted to the coded sequence L based on quaternary tree；
C. a series of basic logic operations that the bianry image logical operation carried out will be needed to be converted to bianry images, that is, have two
The basic dyadic logical operation of a operand, including logic and operation AND, logic or operation OR, logic XOR operation XOR and
Logic difference operation SUB, and the basic monadic logic operation with single operation number, i.e. logic inverse operation NOT；For two-value
Image I, logical not operation NOT's the result is that a width and I same sizes bianry image I R, i.e. I R=NOTI.
The present invention improves measurement accuracy by the calculation of bianry image numerical value, and the threedimensional model image of generation is more forced
Very.
The present invention by remote sensing monitoring, automatic monitoring and it is artificial patrol survey it is hidden to city can coagulation zone domain be difficult to be monitored,
It can not achieve to understanding concrete condition, be monitoring blind area, it is simple and convenient, and by data analytical precision higher.
Embodiment 3
Urban changes solid Remote Sensing Images Matching suspicious region Automated inspection method, it is characterised in that：Include the following steps：
A. remote sensing monitoring, automatic monitoring were used and manually patrols three kinds of approach of survey and obtains and was referred to the relevant monitoring of city suspicious region
Data, including asd number and positioning index, air quality indexes, temperature index, passerby's quantitative index and video image are marked, are obtained
The data taken pass through the Internet transmission to data center；
B. backup and data processing are carried out to the data of data center；
C. Three-dimension Numerical Model is built according to the data source stored in database, including is particularly shown building situation, air matter
Amount, temperature data, number synthesize a solid figure.
D. edge extracting and refinement based on the left image of stereogram obtain the bianry image of Single pixel edge；
E. bianry image is controlled and is measured：The multiple picture control points laid in measured region are measured respectively, and
Obtain control survey of aerial photograph data；
F. dem data acquisition and dem data collection editor are carried out to measurement data, just completes the DEM digital elevations of measured region
The manufacturing process of model.
Bianry image in the step D includes the following steps：
A. bianry image I O are extended for the square-shaped image that the length of side is equal to 4 N, extending method is to increase 4N- in the lower section of I O
H rows, all logical zeros of pixel value in increased row, i.e. black color dots；Then the image after having carried out above-mentioned row and having expanded again
Right increase 6N-W row, the also all logical zeros of the pixel value in increased row；
B., I is converted to the coded sequence L based on quaternary tree；
C. a series of basic logic operations that the bianry image logical operation carried out will be needed to be converted to bianry images, that is, have two
The basic dyadic logical operation of a operand, including logic and operation AND, logic or operation OR, logic XOR operation XOR and
Logic difference operation SUB, and the basic monadic logic operation with single operation number, i.e. logic inverse operation NOT；For two-value
Image I, logical not operation NOT's the result is that a width and I same sizes bianry image I R, i.e. I R=NOTI.
Dem data obtains in the step F and editor includes the following steps：
A. the dem data of extraction zoning and closed area boundary, and calculate intersecting point coordinate；
B. by intersection point data prediction, judge putting in order for intersection point, calculate the ranks number where intersection point；
C. intersection point puts in order, and most northern in zoning, most eastern, most southern, most Western-style pastry coordinate counts this four points in intersection point
Then arrangement position in file judges putting in order for intersection point according to arrangement position；
D. calculated according to the effective area for carrying out borderline region that puts in order, by section successively based on into edlin.
The present invention improves measurement accuracy by the calculation of bianry image numerical value, and the threedimensional model image of generation is more forced
Very.
The present invention by remote sensing monitoring, automatic monitoring and it is artificial patrol survey it is hidden to city can coagulation zone domain be difficult to be monitored,
It can not achieve to understanding concrete condition, be monitoring blind area, it is simple and convenient, and by data analytical precision higher.
The influence that the present invention was obtained and edited can be quickly by life entity to topographic map by dem data is eliminated,
So as to obtain more accurate topographic map.
The above is only presently preferred embodiments of the present invention, not does limitation in any form to the present invention, it is every according to
According to the technical spirit of the present invention to any simple modification, equivalent variations made by above example, the protection of the present invention is each fallen within
Within the scope of.
Claims (3)
1. urban changes solid Remote Sensing Images Matching suspicious region Automated inspection method, it is characterised in that：Include the following steps：
A. remote sensing monitoring, automatic monitoring were used and manually patrols three kinds of approach of survey and obtains and was referred to the relevant monitoring of city suspicious region
Data, including asd number and positioning index, air quality indexes, temperature index, passerby's quantitative index and video image are marked, are obtained
The data taken pass through the Internet transmission to data center；
B. backup and data processing are carried out to the data of data center；
C. Three-dimension Numerical Model is built according to the data source stored in database, including is particularly shown building situation, air matter
Amount, temperature data, number synthesize a solid figure；
D. edge extracting and refinement based on the left image of stereogram obtain the bianry image of Single pixel edge；
E. bianry image is controlled and is measured：The multiple picture control points laid in measured region are measured respectively, and
Obtain control survey of aerial photograph data；
F. dem data acquisition and dem data collection editor are carried out to measurement data, just completes the DEM digital elevations of measured region
The manufacturing process of model.
2. urban changes solid Remote Sensing Images Matching suspicious region Automated inspection method as described in claim 1, feature
It is：Bianry image in the step D includes the following steps：
A. bianry image I O are extended for the square-shaped image that the length of side is equal to 4 N, extending method is to increase 4N- in the lower section of I O
H rows, all logical zeros of pixel value in increased row, i.e. black color dots；Then the image after having carried out above-mentioned row and having expanded again
Right increase 6N-W row, the also all logical zeros of the pixel value in increased row；
B., I is converted to the coded sequence L based on quaternary tree；
C. a series of basic logic operations that the bianry image logical operation carried out will be needed to be converted to bianry images, that is, have two
The basic dyadic logical operation of a operand, including logic and operation AND, logic or operation OR, logic XOR operation XOR and
Logic difference operation SUB, and the basic monadic logic operation with single operation number, i.e. logic inverse operation NOT；For two-value
Image I, logical not operation NOT's the result is that a width and I same sizes bianry image I R, i.e. I R=NOTI.
3. urban changes solid Remote Sensing Images Matching suspicious region Automated inspection method as described in claim 1, feature
It is：Dem data obtains in the step F and editor includes the following steps：
A. the dem data of extraction zoning and closed area boundary, and calculate intersecting point coordinate；
B. by intersection point data prediction, judge putting in order for intersection point, calculate the ranks number where intersection point；
C. intersection point puts in order, and most northern in zoning, most eastern, most southern, most Western-style pastry coordinate counts this four points in intersection point
Then arrangement position in file judges putting in order for intersection point according to arrangement position；
D. calculated according to the effective area for carrying out borderline region that puts in order, by section successively based on into edlin.
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| CN201810441425.4A CN108648275A (en) | 2018-05-10 | 2018-05-10 | Urban changes solid Remote Sensing Images Matching suspicious region Automated inspection method |
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101901343A (en) * | 2010-07-20 | 2010-12-01 | 同济大学 | Remote sensing image road extracting method based on stereo constraint |
| 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 |
| CN104574449A (en) * | 2015-01-27 | 2015-04-29 | 国家测绘地理信息局大地测量数据处理中心 | DEM-based projection area calculation method |
| CN105608713A (en) * | 2016-01-04 | 2016-05-25 | 湖南大学 | Quadtree-based binary image coding and high-efficiency logical operation method |
| CN107340365A (en) * | 2017-06-19 | 2017-11-10 | 中国科学院南京地理与湖泊研究所 | A kind of three-dimensional monitoring and data digging system and method towards lake blue algae disaster |
-
2018
- 2018-05-10 CN CN201810441425.4A patent/CN108648275A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101901343A (en) * | 2010-07-20 | 2010-12-01 | 同济大学 | Remote sensing image road extracting method based on stereo constraint |
| 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 |
| CN104574449A (en) * | 2015-01-27 | 2015-04-29 | 国家测绘地理信息局大地测量数据处理中心 | DEM-based projection area calculation method |
| CN105608713A (en) * | 2016-01-04 | 2016-05-25 | 湖南大学 | Quadtree-based binary image coding and high-efficiency logical operation method |
| CN107340365A (en) * | 2017-06-19 | 2017-11-10 | 中国科学院南京地理与湖泊研究所 | A kind of three-dimensional monitoring and data digging system and method towards lake blue algae disaster |
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