CN111487643B - Building detection method based on laser radar point cloud and near-infrared image - Google Patents
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Abstract
The invention discloses a building detection method based on laser radar point cloud and near-infrared images, which comprises the following steps: acquiring an ortho-image and a laser radar point cloud with a near-infrared band, and registering and fusing the ortho-image and the laser radar point cloud; calculating the normalized vegetation index NDVI of each laser point after fusion, and finishing vegetation identification based on the NDVI and a support vector machine classifier; for the non-vegetation laser points, completing building identification through a neighbor search algorithm and a height threshold; for the building laser points, extracting roof seed points and candidate facade points, and acquiring a roof point group based on an individual building based on the roof seed points; estimating a vertical facade of the building based on the group of roof points for each building; based on the candidate vertical face points and the estimated vertical face, finely extracting the vertical face points; and finishing the detection of the three-dimensional building through the roof points and the elevation points obtained by fine extraction. The invention effectively improves the detection precision of the building and can ensure higher detail level of the building model.
Description
Technical Field
The invention relates to the technical field of remote sensing ground object three-dimensional extraction, in particular to a building detection method based on laser radar point cloud and near-infrared images.
Background
The building detection by utilizing the remote sensing data has good application in various fields such as city planning, real estate and land utilization analysis. Conceptually, building detection is a classification problem that requires separating the building from other objects, such as vehicles, the ground (roads, lawns), and vegetation (trees and shrubs). Existing building detection algorithms can be classified into three major categories based on the difference in input data.
Firstly, the method based on aerial images, namely, the method of image segmentation based on pixels and objects is adopted to extract buildings. The pixel-based heterogeneous approach, whether supervised or unsupervised learning is employed, has the advantage that the complexity is always low, but it is difficult to use high-resolution data due to the high complexity and variability of spectra and textures in urban scenes. The initial object-based approach, which mainly uses the spectral similarity and homogeneity of pixels to identify objects, has better performance than the pixel-based approach, but it is difficult to determine the optimal scale level. In addition, the main limitation of building detection using images is that only two-dimensional contours of buildings can be identified.
Secondly, the buildings are probed using the original ALS (Airborne Laser scanners) point cloud. ALS point clouds have been used more frequently for building detection due to their high pulse frequency, high vertical resolution, and the ability to provide spatial shape information directly, but such methods use only spatial and intensity features, neglecting texture and spectral considerations, and tend to confuse trees and buildings with smooth crowns.
The third category is to fuse the point cloud and the multi-source data of aerial imaging to carry out building detection. Rottensteiner et al, "Rottensteiner, F; trinder, J.; clode, S.; the method has the main limitations that the resolution of the laser point cloud is reduced by an interpolation dsm data source, and the laser point cloud is output to a building area instead of a marked point cloud. Awrangjeb et al, in "Awrangjeb, M.; ravanbakhsh, m.; in Fraser, c.s.automatic detection of residual building using LIDAR data and multispectral image, isprs j.photoprammm. remote sens.2010, 65, 457-.
Disclosure of Invention
The invention aims to provide a building detection method based on laser radar point cloud and near-infrared images, which aims to solve the problems that in the prior art, the error fraction between buildings and trees in a high vegetation coverage area is high, and a high-precision detection method of a building scanning point hierarchy is lacked, and can effectively improve the building detection precision.
In order to achieve the purpose, the invention provides the following scheme: the invention provides a building detection method based on laser radar point cloud and near-infrared images, which comprises the following steps:
acquiring an ortho-image and a laser radar point cloud with near-infrared bands, and registering and fusing the ortho-image and the laser radar point cloud;
calculating a normalized vegetation index NDVI of each laser point in the fused laser radar point cloud, and completing coarse identification of vegetation in the laser radar point cloud based on the NDVI and a Support Vector Machine (SVM) classifier;
for non-vegetation laser points in the laser radar point cloud, the division of the ground, low and short buildings and buildings is completed through a neighbor search algorithm and a height threshold value;
for building laser points in the laser radar point cloud, extracting roof seed points and candidate facade points, and acquiring a roof point group based on an individual building based on the roof seed points;
estimating a vertical facade of the building based on the group of roof points for each building; based on the candidate vertical face points and the estimated vertical face, adopting a distance threshold value method to carry out fine extraction on the vertical face points; and finishing the detection of the three-dimensional building through the roof points and the elevation points obtained by fine extraction.
Preferably, the specific method for registration and fusion of the ortho-image and the laser radar point cloud comprises the following steps:
and aiming at each laser point in the laser radar point cloud, respectively distributing the pixel values of the near infrared band, the red band and the green band of the nearest pixel in the ortho-image to the laser point, and finishing the fusion of the ortho-image and the laser radar point cloud.
Preferably, the method for roughly identifying vegetation in the laser radar point cloud comprises the following steps:
calculating the NDVI of each laser point according to the near infrared band spectral value NIR and the red band spectral value R of each laser point in the fused laser radar point cloud;
selecting two types of training samples of vegetation and non-vegetation in the laser radar point cloud, calculating the NDVI of each sample, and training an SVM classifier based on the NDVI;
and inputting the NDVI of each laser point in the fused laser radar point cloud into a trained SVM classifier to obtain a recognition result of the vegetation in the laser radar point cloud.
Preferably, the method for dividing the ground, the short buildings and the buildings comprises the following steps:
determining a ground point adjacent to each non-vegetation laser point by using a neighbor search algorithm;
calculating the relative height rh of each non-ground point;
and completing the division of buildings and other low buildings through a preset height threshold value based on the relative height rh of each non-ground point.
Preferably, the candidate facade points and the roof point group acquisition method based on the single building comprise the following steps:
extracting reliable roof seed points and candidate facade points by adopting a threshold classification method based on the surface curvature c of the building laser point, the direction value Nz of the normal vector z and the nearest Echo information Echo;
based on the roof seed points, a region growing algorithm and an image segmentation algorithm are adopted to obtain a roof point group belonging to each single building.
Preferably, the fine extraction method of the building facade points comprises the following steps:
based on the roof point group of each building, adopting a roof boundary tracking algorithm and a regularization algorithm to estimate a vertical facade; and calculating the maximum normal distance from the candidate facade point to the estimated vertical facade closest to the candidate facade point, if the maximum normal distance is greater than a preset threshold value, marking the candidate facade point as a false facade point, otherwise, marking the candidate facade point as a real facade point of the current roof segment, and finishing the fine extraction of the facade point of the building.
The invention discloses the following technical effects:
(1) the invention effectively separates the building from the dense vegetation around by adopting the NDVI index, and well improves the segmentation precision of the building and the vegetation. The experimental result on the test data set shows that the correctness and the integrity of the invention reach more than 92 percent no matter the point cloud layer or the building object layer is detected by the invention.
(2) Compared with the existing building detection method, the method provided by the invention has the advantages that the vertex and the elevation point of each building object are detected by integrating the laser point cloud data and the multiband orthographic image with the near infrared band, and an object class label can be distributed to each point, so that the geometric reconstruction of the single building model reaches a relatively high detail level.
(3) The building detection method has high automation degree, and is a high vegetation coverage area building extraction method with great practical prospect.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a building detection method based on laser radar point cloud and near infrared images according to the present invention;
fig. 2 is an intermediate result diagram of the verification of the building detection method according to the present invention by the test data set in the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Referring to fig. 1, the embodiment provides a building detection method based on a laser radar point cloud and a near-infrared image, including the following steps:
step S1, acquiring an ortho-image and a laser radar point cloud with near-infrared wave bands, and registering and fusing the ortho-image and the laser radar point cloud; the method specifically comprises the following steps:
aiming at each laser point in the laser radar point cloud, respectively distributing the pixel values of the near infrared band, the red band and the green band of the nearest pixel in the ortho-image to the laser point to complete the fusion of the ortho-image and the laser radar point cloud; in the fusion result, each laser spot includes 9 attributes: x coordinate value, y coordinate value, z coordinate value, echo intensity, the second time echo, total times echo, near infrared band spectrum value NIR, red band spectrum value R and green band spectrum value G.
Step S2, calculating the NDVI (Normalized Vegetation Index) of each laser point in the fused laser radar point cloud, and completing coarse identification of Vegetation in the laser radar point cloud based on the NDVI and a SVM (support vector machine) classifier; the method specifically comprises the following steps:
calculating the NDVI of each laser point according to the near infrared band spectral value NIR and the red band spectral value R of each laser point in the fused laser radar point cloud, wherein the formula (1) is as follows:
NDVI＝(NIR-R)/(NIR+R)………………………………(1)
selecting two types of training samples of vegetation and non-vegetation in the laser radar point cloud, calculating the NDVI of each sample according to formula (1), and training an SVM classifier based on the NDVI;
and inputting the NDVI of each laser point in the fused laser radar point cloud into a trained SVM classifier to obtain a recognition result of the vegetation in the laser radar point cloud, and completing coarse recognition of the vegetation.
S3, for non-vegetation laser points in the laser radar point cloud, dividing the ground, low and short buildings and buildings through a neighbor search algorithm and a height threshold value; the method specifically comprises the following steps:
firstly, determining a ground point adjacent to each non-vegetation laser point by using a neighbor search algorithm;
secondly, calculating the relative height rh of each non-ground point, as shown in the formula (2);
wherein
Is the elevation of the current non-ground point,
is the elevation of the jth neighboring ground point of the current non-ground point, N is the number of neighboring ground points of the current non-ground point, j is 1, 2, …, N;
thirdly, based on the relative height rh of each non-ground point, the division of buildings and other low buildings is completed through a preset height threshold, and the height threshold of the embodiment is set to be 2.5 meters.
S4, extracting roof seed points and candidate vertical surface points for building laser points in the laser radar point cloud, and acquiring a roof point group based on an individual building based on the roof seed points; the method specifically comprises the following steps:
for a point cloud set only containing a building laser point, firstly, extracting reliable roof seed points and candidate facade points by adopting a threshold classification method based on the surface curvature c of the building laser point, the direction value Nz of a normal vector z and the nearest Echo information Echo; and then, based on the roof seed points, obtaining a roof point group belonging to each single building by adopting a region growing algorithm and an image segmentation algorithm.
Step S5, estimating the vertical facade of the building based on the roof point group of each building; based on the candidate vertical face points and the estimated vertical face, adopting a distance threshold value method to carry out fine extraction on the vertical face points; and finishing the detection of the three-dimensional building through the roof points and the elevation points obtained by fine extraction. The method specifically comprises the following steps:
based on the roof point group of each building, adopting a roof boundary tracking algorithm and a regularization algorithm to estimate a vertical facade; calculating the maximum normal distance from the candidate facade point to the estimated vertical facade closest to the candidate facade point, if the maximum normal distance is larger than a preset threshold value, marking the candidate facade point as a false facade point, otherwise, marking the candidate facade point as a real facade point of the current roof segment; and completing the detection of the three-dimensional building through the roof vertex and the real facade point.
In order to further verify the accuracy of the building detection method, the building detection method is verified through a test data set, and a result schematic diagram of each step is shown in FIG. 2; wherein, the graph (a) is an original laser radar point cloud plane projection graph, and the points with similar heights have similar colors; the figure (b) is a laser radar point cloud three-dimensional figure which is colored according to the spectral value of the ortho-image; the figure (c) is a laser radar point cloud three-dimensional figure which is colored according to the NDVI value; the diagram (d) is a mask for carrying out vegetation rough extraction by utilizing an SVM; graph (e) is the ground and low targets extracted using a neighbor search algorithm and a height threshold technique; FIG. 2(f) is a reliable rooftop seed point classified by using surface curvature c, direction value Nz of normal vector z and nearest echo information; graph (g) is the result of clustering and filtering candidate objects on the building roof using region growing and region threshold segmentation algorithms; and (h) is the extracted roof of the building. Through the verification of a test data set, the building detection method based on the laser radar point cloud and the near-infrared image has the advantages that the correctness and the integrity reach more than 92 percent when the building is detected, and the geometric reconstruction of the single building model reaches a relatively high detail level.
In the description of the present invention, it is to be understood that the terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, are merely for convenience of description of the present invention, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention.
The above-described embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solutions of the present invention can be made by those skilled in the art without departing from the spirit of the present invention, and the technical solutions of the present invention are within the scope of the present invention defined by the claims.
Claims (6)
1. A building detection method based on laser radar point cloud and near-infrared images is characterized by comprising the following steps:
acquiring an ortho-image and a laser radar point cloud with near-infrared bands, and registering and fusing the ortho-image and the laser radar point cloud;
calculating a normalized vegetation index NDVI of each laser point in the fused laser radar point cloud, and completing coarse identification of vegetation in the laser radar point cloud based on the NDVI and a Support Vector Machine (SVM) classifier;
for non-vegetation laser points in the laser radar point cloud, the division of the ground, low and short buildings and buildings is completed through a neighbor search algorithm and a height threshold value;
for building laser points in the laser radar point cloud, extracting roof seed points and candidate facade points, and acquiring a roof point group based on an individual building based on the roof seed points;
estimating a vertical facade of the building based on the group of roof points for each building; based on the candidate vertical face points and the vertical face obtained by estimation, finely extracting the vertical face points by adopting a distance threshold method; and finishing the detection of the three-dimensional building through the roof points and the elevation points obtained by fine extraction.
2. The building detection method based on the lidar point cloud and the near-infrared image as claimed in claim 1, wherein the specific method for registering and fusing the orthoimage and the lidar point cloud comprises:
and aiming at each laser point in the laser radar point cloud, respectively distributing the pixel values of the near infrared band, the red band and the green band of the nearest pixel in the ortho-image to the laser point, and finishing the fusion of the ortho-image and the laser radar point cloud.
3. The building detection method based on the lidar point cloud and the near-infrared image according to claim 1, wherein the rough identification method of vegetation in the lidar point cloud comprises:
calculating the NDVI of each laser point according to the near infrared band spectral value NIR and the red band spectral value R of each laser point in the fused laser radar point cloud;
selecting two types of training samples of vegetation and non-vegetation in the laser radar point cloud, calculating the NDVI of each sample, and training an SVM classifier based on the NDVI;
and inputting the NDVI of each laser point in the fused laser radar point cloud into a trained SVM classifier to obtain a recognition result of the vegetation in the laser radar point cloud.
4. The building detection method based on the lidar point cloud and the near-infrared image as claimed in claim 1, wherein the method for dividing the ground, the low and short buildings and the building comprises:
determining a ground point adjacent to each non-vegetation laser point by using a neighbor search algorithm;
calculating the relative height rh of each non-ground point;
and completing the division of buildings and other low buildings through a preset height threshold value based on the relative height rh of each non-ground point.
5. The lidar point cloud and near-infrared image-based building detection method of claim 1, wherein the candidate facade points and the individual building-based roof point group acquisition method comprise:
extracting reliable roof seed points and candidate facade points by adopting a threshold classification method based on the surface curvature c of the building laser point, the direction value Nz of the normal vector z and the nearest Echo information Echo;
based on the roof seed points, a region growing algorithm and an image segmentation algorithm are adopted to obtain a roof point group belonging to each single building.
6. The building detection method based on the lidar point cloud and the near-infrared image as claimed in claim 1, wherein the method for finely extracting the building facade points comprises the following steps:
based on the roof point group of each building, adopting a roof boundary tracking algorithm and a regularization algorithm to estimate a vertical facade; and calculating the maximum normal distance from the candidate facade point to the estimated vertical facade closest to the candidate facade point, if the maximum normal distance is greater than a preset threshold value, marking the candidate facade point as a false facade point, otherwise, marking the candidate facade point as a real facade point of the current roof segment, and finishing the fine extraction of the facade point of the building.
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| CN114764871B (en) * | 2022-06-15 | 2022-09-13 | 青岛市勘察测绘研究院 | Urban building attribute extraction method based on airborne laser point cloud |
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102103202A (en) * | 2010-12-01 | 2011-06-22 | 武汉大学 | Semi-supervised classification method for airborne laser radar data fusing images |
| US8582808B2 (en) * | 2011-06-16 | 2013-11-12 | CSC Holdings, LLC | Methods for identifying rooftops using elevation data sets |
| CN105469098A (en) * | 2015-11-20 | 2016-04-06 | 中北大学 | Precise LINDAR data ground object classification method based on adaptive characteristic weight synthesis |
| CN106529484A (en) * | 2016-11-16 | 2017-03-22 | 哈尔滨工业大学 | Combined spectrum and laser radar data classification method based on class-fixed multinucleated learning |
| CN110309780A (en) * | 2019-07-01 | 2019-10-08 | 中国科学院遥感与数字地球研究所 | High resolution image houseclearing based on BFD-IGA-SVM model quickly supervises identification |
| CN110569745A (en) * | 2019-08-20 | 2019-12-13 | 北方工业大学 | remote sensing image building area detection method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10025983B2 (en) * | 2015-09-21 | 2018-07-17 | The Climate Corporation | Ponding water detection on satellite imagery |
-
2020
- 2020-04-13 CN CN202010283212.0A patent/CN111487643B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102103202A (en) * | 2010-12-01 | 2011-06-22 | 武汉大学 | Semi-supervised classification method for airborne laser radar data fusing images |
| US8582808B2 (en) * | 2011-06-16 | 2013-11-12 | CSC Holdings, LLC | Methods for identifying rooftops using elevation data sets |
| CN105469098A (en) * | 2015-11-20 | 2016-04-06 | 中北大学 | Precise LINDAR data ground object classification method based on adaptive characteristic weight synthesis |
| CN106529484A (en) * | 2016-11-16 | 2017-03-22 | 哈尔滨工业大学 | Combined spectrum and laser radar data classification method based on class-fixed multinucleated learning |
| CN110309780A (en) * | 2019-07-01 | 2019-10-08 | 中国科学院遥感与数字地球研究所 | High resolution image houseclearing based on BFD-IGA-SVM model quickly supervises identification |
| CN110569745A (en) * | 2019-08-20 | 2019-12-13 | 北方工业大学 | remote sensing image building area detection method |
Non-Patent Citations (2)
| Title |
|---|
| Automatic Segmentation of Raw LIDAR Data for Extraction of Building Roofs;Mohammad Awrangjeb et al.;《Remote Sensing》;20141231;第6卷(第5期);第3716-3751页 * |
| 基于UAV倾斜影像匹配点云的城市建筑物信息提取方法研究;戴玉成;《中国博士学位论文全文数据库 基础科学辑》;20171015(第10期);正文第80-99页 * |
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