CN104502919A - Method for utilizing airborne laser radar point cloud to extract urban vegetation three-dimensional coverage map - Google Patents
Method for utilizing airborne laser radar point cloud to extract urban vegetation three-dimensional coverage map Download PDFInfo
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- CN104502919A CN104502919A CN201510019559.3A CN201510019559A CN104502919A CN 104502919 A CN104502919 A CN 104502919A CN 201510019559 A CN201510019559 A CN 201510019559A CN 104502919 A CN104502919 A CN 104502919A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
Abstract
The invention discloses a method for utilizing airborne laser radar point cloud to extract urban vegetation three-dimensional coverage map. The method comprises the following steps of data pro-processing, airborne laser radar (LiDAR) point cloud data median filtering and data height normalization. A classification rule based on a strength value and space information is established according to a result analyzed after a ground feature laser point strength value is calibrated according to the aerial photograph condition and is used for distinguishing vegetation laser points and non-vegetation laser points. An existing tool is utilized to evaluate the effectiveness of the rule, and laser point classification is performed after optimization. The laser points are vertically stratified, and an inverse distance method (IDW) is utilized to conduct interpolation on stratified data to obtain a vegetation three-dimensional coverage map.
Description
Technical field
The present invention relates to a kind of method utilizing airborne laser radar point cloud to extract the covering of urban vegetation three-dimensional, the technical field of the invention is resource and environment remote sensing or digital image processing techniques field, is particularly useful for the three-dimensional vegetative coverage geographic information data of ecocity and extracts.
Background technology
In urban vegetation coverage information extracts, there is the problems such as massif vegetation projected area diminishes in application of high resolution image relative maturity, but the covering distribution that can only obtain vegetation plane.Flourish along with airborne laser radar (LiDAR) technology in recent years, makes Chinese scholars carry out the three-dimensional vegetative coverage in city based on LiDAR point cloud and the mode that is combined with image and studies and become possibility.Obtain the three-dimensional vegetative coverage figure in city, the assessment ecocity that is conducive to becoming more meticulous relates to the index system of vegetation or greening, improves and life of urban resident is significant to urban environment.
Airborne laser radar (LiDAR) is the airborne lidar of integrated global global position system (GPS) and inertial measuring unit (IMU) technology, can obtain the three-dimensional coordinate of ground object and other for information about.The laser pulse energy partial penetration tree crown of airborne LiDAR sensor emission blocks, and affects very little by luminous ray, directly obtains high-precision three-dimensional earth's surface topographic space data.The data that LiDAR technology obtains are through aftertreatment, the mapping products such as high-precision digital elevation model (DEM), contour map (CM) and numerical cutting tool (DSM) can be generated, there is the superiority that traditional photography is measured and ground routine measuring technique cannot replace.
By laser point cloud data process, in conjunction with DEM and DSM achievement, the two-dimentional urban afforestation coverage information the same with remote sensing multispectral data can be obtained.And due to the primary three-dimensional feature of laser radar, nature can obtain three-dimensional city greening coverage diagram.
Summary of the invention
Goal of the invention: the present invention proposes a kind of method utilizing airborne laser radar point cloud to extract the covering of urban vegetation three-dimensional, the three-dimensional coverage information of effective extraction urban vegetation, obtain three-dimensional overlay, solve urban vegetation to cover in investigation and use bidimensional image that surface area is diminished problem, be conducive to research and how improve the ecological living environment quality of city dweller.
Technical scheme: utilize airborne laser radar point cloud to extract the method for urban vegetation three-dimensional covering, comprise the steps:
The first step, data prediction.Set up the median filter based on airborne laser radar (LiDAR) cloud data strength information, utilize this median filter to cloud data filtering, remove intensity noise.Deduct ground elevation (DEM) value by the height value of laser spots data, height normalization, removes the impact of surface relief.Comprise following sub-step:
(1) according to the density case of original point cloud, division rule graticule mesh (graticule mesh step-length D), makes a cloud drop in graticule mesh, thus sets up spatial index framework.
(2) obtain the point of maximum intensity value in graticule mesh, preserve its coordinate and intensity level, form a data set.
(3) by some cloud intensity level from big to small the institute that concentrates of sorting data a little, and judge whether the maximum point of intensity level is less than given cutoff threshold (I): if be more than or equal to I value, then perform next step; If be less than I value, then calculate end.
(4) centered by data centralization first point, certain radius (R) is under spatial index framework, search laser spots, sorts by its intensity level from big to small to the laser spots searched, and replaces the intensity level of search center point by the intensity intermediate value of this sequence of points.
(5) upgrade the intensity level of data centralization and the intensity level of search framework mid point, repeat (3) step, terminate until calculate.
(6) output intensity median-filtered result.
Second step, atural object laser spots intensity level calibration post analysis.The intensity level of atural object of the same race LiDAR point cloud in different measuring distance is not identical, and main and distance dependent, utilizes formula calibration intensity.In the urban area of a Sortie, select typical feature according to image, analyze the feature of its reflection LiDAR intensity, therefrom find out the universal law utilizing LiDAR intensity to extract atural object.
3rd step, sets up the classifying rules according to intensity level and spatial information, is used for classification vegetation and non-vegetation.Comprise following sub-step:
(1) after the normalization of the pile such as tall and big vegetation and house, height value is comparatively large, but the strength criterion deviation value of building is little and vegetation cover strength standard deviation value large, and except special material, the strength mean value of vegetation is greater than building.
(2) after the normalization of the man-made features such as grassland vegetation and road, height value is less, but the strength mean value of the vegetation such as meadow is large, and strength criterion deviation value is also large; The intensity of usual road is all little with strength criterion deviation value, and indivedual paved road strength mean value is large, and strength criterion deviation value is little.
(3) the pure water surface does not have laser spots, has that the water surface of foreign material or hydrophyte point cloud strength mean value is little but strength criterion deviation value is large.
(4) the vegetation growth careless symbiosis of general tall filling or single vegetation in flakes, consider the continuity of atural object when selecting laser spots intensity threshold.
4th step, utilizes the validity of existing tools assessment rule, and after being optimized, carries out laser point classification.Utilize the multi-spectrum remote sensing image had, whether containing rough error in comparative analysis classifying rules; Terrasolid software is utilized to carry out manual sort to sample place cloud, classification rule.
5th step, laser spots vertical demixing, utilizes anti-Furthest Neighbor (IDW) to individual-layer data interpolation respectively, obtains vegetation three-dimensional overlay.
Beneficial effect: compared with prior art, method provided by the present invention has the following advantages: invent and employ the wave filter being suitable for the denoising of laser point cloud intensity; The three-dimensional vegetative coverage in urban area can be extracted according to laser point cloud data.
Accompanying drawing explanation
Fig. 1 is that the present invention utilizes airborne laser radar point cloud to extract the process flow diagram of the method that urban vegetation three-dimensional covers.
Fig. 2 is laser spots intensity distribution of the present invention, and (a) figure is before filtering, and (b) figure is filtered.
Fig. 3 is three dimensional separation face figure, (a) figure of the present invention is some cloud front elevation, and (b) figure is division surface front elevation, and (c) figure is sectional view.
Fig. 4 is that urban vegetation of the present invention three-dimensional covers skeleton view.
Embodiment
Below in conjunction with the drawings and specific embodiments, illustrate the present invention further, these embodiments should be understood only be not used in for illustration of the present invention and limit the scope of the invention, after having read the present invention, the amendment of those skilled in the art to the various equivalent form of value of the present invention has all fallen within the application's claims limited range.
Method flow according to Fig. 1, for application example the present invention is illustrated further with " greening of Nanjing park for peace three-dimensional covers and extracts ":
The first step, data prediction.Set up the median filter based on airborne laser radar (LiDAR) cloud data strength information, utilize this median filter to cloud data filtering, remove intensity noise.Deduct ground elevation (DEM) value by the height value of laser spots data, height normalization, removes the impact of surface relief.Comprise following sub-step:
(1) according to the density case of original point cloud, division rule graticule mesh (graticule mesh step-length D is set to 3 meters), makes a cloud drop in graticule mesh, thus sets up spatial index framework.
(2) obtain the point of maximum intensity value in graticule mesh, preserve its coordinate and intensity level, form a data set.
(3) by some cloud intensity level from big to small the institute that concentrates of sorting data a little, and judge whether the maximum point of intensity level is less than given cutoff threshold (I): if be more than or equal to I value, then perform next step; If be less than I value, then calculate end.The determination of I value, relevant with characters of ground object.This routine value is 20.
(4) centered by data centralization first point, certain radius (R is set to 2 meters), under spatial index framework, searches for laser spots, the laser spots searched is sorted from big to small by its intensity level, replaces the intensity level of search center point by the intensity intermediate value of this sequence of points.
(5) upgrade the intensity level of data centralization and the intensity level of search framework mid point, repeat (3) step, terminate until calculate.
(6) output intensity median-filtered result.
Second step, atural object laser spots intensity level calibration post analysis.The intensity level of atural object of the same race LiDAR point cloud in different measuring distance is not identical, and main and distance dependent, utilizes formula calibration intensity.
for being normalized to standard R
sapart from upper intensity level, I is the intensity level of actual measurement, and R is the distance of sensor and the detection of a target.In the urban area of a Sortie, select typical feature according to image, analyze the feature of its reflection LiDAR intensity, therefrom find out the universal law utilizing LiDAR intensity to extract atural object.This example finds out 12 kinds of atural objects, specifically sees the following form (table 1):
Table 1 clutter reflections LiDAR strength characteristics
Old factory building room, general factory building, resident's building, calm water, highway (pitch) intensity level are less, and natural forest, natural forest, natural meadow, artificial lawn, paved road, highway (concrete) intensity level are larger.No matter these vegetation cover strength values are large or little, the tension variance of vegetation is large, and other atural objects are little.Regularity wherein proves to extract urban afforestation coverage information according to clutter reflections LiDAR point intensity level.
3rd step, sets up the classifying rules according to intensity level and spatial information, is used for classification vegetation and non-vegetation.Comprise following sub-step:
(1) after the normalization of the pile such as tall and big vegetation and house, height value is comparatively large, but the strength criterion deviation value of building is little and vegetation cover strength standard deviation value large, and except special material, the strength mean value of vegetation is greater than building.
(2) after the normalization of the man-made features such as grassland vegetation and road, height value is less, but the strength mean value of the vegetation such as meadow is large, and strength criterion deviation value is also large; The intensity of usual road is all little with strength criterion deviation value, and indivedual paved road strength mean value is large, and strength criterion deviation value is little.
(3) the pure water surface does not have laser spots, has that the water surface of foreign material or hydrophyte point cloud strength mean value is little but strength criterion deviation value is large.
(4) the vegetation growth careless symbiosis of general tall filling or single vegetation in flakes, consider the continuity of atural object when selecting laser spots intensity threshold.
4th step, utilizes the validity of existing tools assessment rule, and after being optimized, carries out laser point classification.Utilize the multi-spectrum remote sensing image had, whether containing rough error in comparative analysis classifying rules; Terrasolid software is utilized to carry out manual sort to sample place cloud, classification rule.
5th step, laser spots vertical demixing, utilizes anti-Furthest Neighbor (IDW) to individual-layer data interpolation respectively, obtains vegetation three-dimensional overlay.
Claims (3)
1. utilize airborne laser radar point cloud to extract the method for urban vegetation three-dimensional covering, tool is characterised in that, comprises the steps:
The first step, data prediction.Set up the median filter based on airborne laser radar (LiDAR) cloud data strength information, utilize this median filter to cloud data filtering, remove intensity noise.Deduct ground elevation (DEM) value by the height value of laser spots data, height normalization, removes the impact of surface relief.
Second step, atural object laser spots intensity level calibration post analysis.The intensity level of atural object of the same race LiDAR point cloud in different measuring distance is not identical, and main and distance dependent, utilizes formula calibration intensity.In the urban area of a Sortie, select typical feature according to image, analyze the feature of its reflection LiDAR intensity, therefrom find out the universal law utilizing LiDAR intensity to extract atural object.
3rd step, sets up the classifying rules according to intensity level and spatial information, is used for classification vegetation and non-vegetation.
4th step, utilizes the validity of existing tools assessment rule, and after being optimized, carries out laser point classification.Utilize the multi-spectrum remote sensing image had, whether containing rough error in comparative analysis classifying rules; Terrasolid software is utilized to carry out manual sort to sample place cloud, classification rule.
5th step, laser spots vertical demixing, utilizes anti-Furthest Neighbor (IDW) to individual-layer data interpolation respectively, obtains vegetation three-dimensional overlay.
2. utilize airborne laser radar point cloud to extract the method for urban vegetation three-dimensional covering as claimed in claim 1, it is characterized in that, the first step comprises following sub-step:
(1) according to the density case of original point cloud, division rule graticule mesh (graticule mesh step-length D), makes a cloud drop in graticule mesh, thus sets up spatial index framework.
(2) obtain the point of maximum intensity value in graticule mesh, preserve its coordinate and intensity level, form a data set.
(3) by some cloud intensity level from big to small the institute that concentrates of sorting data a little, and judge whether the maximum point of intensity level is less than given cutoff threshold (I): if be more than or equal to I value, then perform next step; If be less than I value, then calculate end.
(4) centered by data centralization first point, certain radius (R) is under spatial index framework, search laser spots, sorts by its intensity level from big to small to the laser spots searched, and replaces the intensity level of search center point by the intensity intermediate value of this sequence of points.
(5) upgrade the intensity level of data centralization and the intensity level of search framework mid point, repeat (3) step, terminate until calculate.
(6) output intensity median-filtered result.
3. utilize airborne laser radar point cloud to extract the method for urban vegetation three-dimensional covering as claimed in claim 1, it is characterized in that, the 3rd step comprises following sub-step:
(1) after the normalization of the pile such as tall and big vegetation and house, height value is comparatively large, but the strength criterion deviation value of building is little and vegetation cover strength standard deviation value large, and except special material, the strength mean value of vegetation is greater than building.
(2) after the normalization of the man-made features such as grassland vegetation and road, height value is less, but the strength mean value of the vegetation such as meadow is large, and strength criterion deviation value is also large; The intensity of usual road is all little with strength criterion deviation value, and indivedual paved road strength mean value is large, and strength criterion deviation value is little.
(3) the pure water surface does not have laser spots, has that the water surface of foreign material or hydrophyte point cloud strength mean value is little but strength criterion deviation value is large.
(4) the vegetation growth careless symbiosis of general tall filling or single vegetation in flakes, consider the continuity of atural object when selecting laser spots intensity threshold.
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Cited By (13)
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|---|---|---|---|---|
| CN105046264A (en) * | 2015-07-08 | 2015-11-11 | 西安电子科技大学 | Sparse surface feature classification and labeling method based on visible light and laser radar images |
| CN106097423A (en) * | 2016-06-08 | 2016-11-09 | 河海大学 | LiDAR point cloud intensity correction method based on k neighbour |
| CN106199562A (en) * | 2016-07-06 | 2016-12-07 | 山东省科学院海洋仪器仪表研究所 | The sea error calibration method of sea-floor relief is measured based on airborne laser radar |
| CN106897686A (en) * | 2017-02-19 | 2017-06-27 | 北京林业大学 | A kind of airborne LIDAR electric inspection process point cloud classifications method |
| CN107421644A (en) * | 2017-08-28 | 2017-12-01 | 南京大学 | The air remote sensing evaluation method of the complete surface temperature in city |
| CN107479065A (en) * | 2017-07-14 | 2017-12-15 | 中南林业科技大学 | A kind of three-dimensional structure of forest gap method for measurement based on laser radar |
| CN108491642A (en) * | 2018-03-27 | 2018-09-04 | 中国科学院遥感与数字地球研究所 | A kind of green degree in floor scale city based on level landscape model perceives measure |
| CN108957444A (en) * | 2018-07-23 | 2018-12-07 | 鲁东大学 | Sea ice region contour line detecting method and device |
| CN109358341A (en) * | 2018-08-31 | 2019-02-19 | 北京理工大学 | A kind of portable Grassland Biomass noninvasive measurement device |
| CN110726998A (en) * | 2019-10-24 | 2020-01-24 | 西安科技大学 | Method for measuring mining subsidence basin in mining area through laser radar scanning |
| CN111308469A (en) * | 2019-11-27 | 2020-06-19 | 北京东方至远科技股份有限公司 | Building elevation measurement method based on PSInSAR technology |
| CN112106370A (en) * | 2018-03-20 | 2020-12-18 | Pcms控股公司 | System and method for optimizing dynamic point clouds based on prioritized transformation |
| CN112595243A (en) * | 2020-12-02 | 2021-04-02 | 中国科学院空天信息创新研究院 | Automatic vegetation plant height measuring method and system suitable for field continuous observation |
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Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN105046264A (en) * | 2015-07-08 | 2015-11-11 | 西安电子科技大学 | Sparse surface feature classification and labeling method based on visible light and laser radar images |
| CN105046264B (en) * | 2015-07-08 | 2018-06-29 | 西安电子科技大学 | A kind of sparse terrain classification and marker method based on visible ray and lidar image |
| CN106097423A (en) * | 2016-06-08 | 2016-11-09 | 河海大学 | LiDAR point cloud intensity correction method based on k neighbour |
| CN106199562A (en) * | 2016-07-06 | 2016-12-07 | 山东省科学院海洋仪器仪表研究所 | The sea error calibration method of sea-floor relief is measured based on airborne laser radar |
| CN106897686A (en) * | 2017-02-19 | 2017-06-27 | 北京林业大学 | A kind of airborne LIDAR electric inspection process point cloud classifications method |
| CN107479065B (en) * | 2017-07-14 | 2020-09-11 | 中南林业科技大学 | Forest gap three-dimensional structure measuring method based on laser radar |
| CN107479065A (en) * | 2017-07-14 | 2017-12-15 | 中南林业科技大学 | A kind of three-dimensional structure of forest gap method for measurement based on laser radar |
| CN107421644A (en) * | 2017-08-28 | 2017-12-01 | 南京大学 | The air remote sensing evaluation method of the complete surface temperature in city |
| CN112106370A (en) * | 2018-03-20 | 2020-12-18 | Pcms控股公司 | System and method for optimizing dynamic point clouds based on prioritized transformation |
| CN108491642A (en) * | 2018-03-27 | 2018-09-04 | 中国科学院遥感与数字地球研究所 | A kind of green degree in floor scale city based on level landscape model perceives measure |
| CN108957444A (en) * | 2018-07-23 | 2018-12-07 | 鲁东大学 | Sea ice region contour line detecting method and device |
| CN108957444B (en) * | 2018-07-23 | 2022-02-01 | 烟台雷奥电子科技有限公司 | Sea ice area contour line detection method and device |
| CN109358341A (en) * | 2018-08-31 | 2019-02-19 | 北京理工大学 | A kind of portable Grassland Biomass noninvasive measurement device |
| CN110726998A (en) * | 2019-10-24 | 2020-01-24 | 西安科技大学 | Method for measuring mining subsidence basin in mining area through laser radar scanning |
| CN111308469A (en) * | 2019-11-27 | 2020-06-19 | 北京东方至远科技股份有限公司 | Building elevation measurement method based on PSInSAR technology |
| CN111308469B (en) * | 2019-11-27 | 2021-07-16 | 北京东方至远科技股份有限公司 | Building elevation measurement method based on PSInSAR technology |
| CN112595243A (en) * | 2020-12-02 | 2021-04-02 | 中国科学院空天信息创新研究院 | Automatic vegetation plant height measuring method and system suitable for field continuous observation |
| CN112595243B (en) * | 2020-12-02 | 2022-05-17 | 中国科学院空天信息创新研究院 | Automatic vegetation plant height measuring method and system suitable for field continuous observation |
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Application publication date: 20150408 |