Classifier-free extraction of power line wires from point cloud data
- Authors: Awrangjeb, Mohammad , Gao, Yongsheng , Lu, Guojun
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018; Canberra, Australia; 10th-13th December 2018
- Full Text: false
- Reviewed:
- Description: This paper proposes a classifier-free method for extraction of power line wires from aerial point cloud data. It combines the advantages of both grid- and point-based processing of the input data. In addition to the non-ground point cloud data, the input to the proposed method includes the pylon locations, which are automatically extracted by a previous method. The proposed method first counts the number of wires in a span between the two successive pylons using two masks: vertical and horizontal. Then, the initial wire segments are obtained and refined iteratively. Finally, the initial segments are extended on both ends and each individual wire points are modelled as a 3D polynomial curve. Experimental results show both the object-based completeness and correctness are 97%, while the point-based completeness and correctness are 99% and 88%, respectively.
- Description: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018
Extracting road centrelines from binary road images by optimizing geodesic lines
- Authors: Zhou, Shaoguang , Lu, Guojun , Teng, Shyh , Zhang, Dengsheng
- Date: 2016
- Type: Text , Conference proceedings , Conference paper
- Relation: 2015 International Conference on Image and Vision Computing New Zealand, IVCNZ 2015; Auckland, New Zealand; 23rd-24th November 2015 Vol. 2016-November, p. 1-6
- Full Text: false
- Reviewed:
- Description: Binary road images can be obtained from remotely sensed images with the aid of classification and segmentation techniques. Extracting road centrelines from these binary images are crucial to update a Geographic Information System (GIS) database. A current state of art method of centreline extraction needs to remove road junctions and depends on the accuracy of the endpoints, leading to three main limitations: (1) causing small gaps in the roads, (2) wrongly treating short non-road segments as roads, and (3) producing centrelines of low accuracy around the road end regions. To overcome these limitations, we propose to use an iteratively searching scheme to obtain the longest geodesic line in the preprocessed road skeleton images. Several image pixels at each end of the geodesic lines were removed to avoid noise, and the remaining parts were optimized using a dynamic programming snake model. The proposed method is applied to three types of binary road images and compared with the state of art method. It shows that the proposed method is less affected by the end regions of the roads, and is effective in filling the gaps in the roads. It also has an advantage on processing short non-road segments. © 2015 IEEE.
- Description: International Conference Image and Vision Computing New Zealand
Building change detection from LIDAR point cloud data based on connected component analysis
- Authors: Awrangjeb, Mohammad , Fraser, Clive , Lu, Guojun
- Date: 2015
- Type: Text , Conference proceedings
- Relation: Isprs Geospatial Week 2015; La Grande Motte, France; 28th September-3rd October 2015; published in International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences Vol. II-3, p. 393-400
- Full Text:
- Reviewed:
- Description: Building data are one of the important data types in a topographic database. Building change detection after a period of time is necessary for many applications, such as identification of informal settlements. Based on the detected changes, the database has to be updated to ensure its usefulness. This paper proposes an improved building detection technique, which is a prerequisite for many building change detection techniques. The improved technique examines the gap between neighbouring buildings in the building mask in order to avoid under segmentation errors. Then, a new building change detection technique from LIDAR point cloud data is proposed. Buildings which are totally new or demolished are directly added to the change detection output. However, for demolished or extended building parts, a connected component analysis algorithm is applied and for each connected component its area, width and height are estimated in order to ascertain if it can be considered as a demolished or new building part. Finally, a graphical user interface (GUI) has been developed to update detected changes to the existing building map. Experimental results show that the improved building detection technique can offer not only higher performance in terms of completeness and correctness, but also a lower number of under-segmentation errors as compared to its original counterpart. The proposed change detection technique produces no omission errors and thus it can be exploited for enhanced automated building information updating within a topographic database. Using the developed GUI, the user can quickly examine each suggested change and indicate his/her decision with a minimum number of mouse clicks.
Automatic building extraction from LIDAR data covering complex urban scenes
- Authors: Awrangjeb, Mohammad , Lu, Guojun , Fraser, Clive
- Date: 2014
- Type: Text , Conference proceedings
- Relation: ISPRS Technical Commission III Symposium; Zurich, Switzerland; 5th-7th September 2014; published in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Vol. XL-3, p. 25-32
- Relation: http://purl.org/au-research/grants/arc/DE120101778
- Full Text:
- Reviewed:
- Description: This paper presents a new method for segmentation of LIDAR point cloud data for automatic building extraction. Using the ground height from a DEM (Digital Elevation Model), the non-ground points (mainly buildings and trees) are separated from the ground points. Points on walls are removed from the set of non-ground points by applying the following two approaches: If a plane fitted at a point and its neighbourhood is perpendicular to a fictitious horizontal plane, then this point is designated as a wall point. When LIDAR points are projected on a dense grid, points within a narrow area close to an imaginary vertical line on the wall should fall into the same grid cell. If three or more points fall into the same cell, then the intermediate points are removed as wall points. The remaining non-ground points are then divided into clusters based on height and local neighbourhood. One or more clusters are initialised based on the maximum height of the points and then each cluster is extended by applying height and neighbourhood constraints. Planar roof segments are extracted from each cluster of points following a region-growing technique. Planes are initialised using coplanar points as seed points and then grown using plane compatibility tests. If the estimated height of a point is similar to its LIDAR generated height, or if its normal distance to a plane is within a predefined limit, then the point is added to the plane. Once all the planar segments are extracted, the common points between the neghbouring planes are assigned to the appropriate planes based on the plane intersection line, locality and the angle between the normal at a common point and the corresponding plane. A rule-based procedure is applied to remove tree planes which are small in size and randomly oriented. The neighbouring planes are then merged to obtain individual building boundaries, which are regularised based on long line segments. Experimental results on ISPRS benchmark data sets show that the proposed method offers higher building detection and roof plane extraction rates than many existing methods, especially in complex urban scenes.
LiDAR segmentation using suitable seed points for 3D building extraction
- Authors: Abdullah, S.M. , Awrangjeb, Mohammad , Lu, Guojun
- Date: 2014
- Type: Text , Conference proceedings
- Full Text: false
- Description: Effective building detection and roof reconstruction has an influential demand over the remote sensing research community. In this paper, we present a new automatic LiDAR point cloud segmentation method using suitable seed points for building detection and roof plane extraction. Firstly, the LiDAR point cloud is separated into "ground" and "non-ground" points based on the analysis of DEM with a height threshold. Each of the non-ground point is marked as coplanar or non-coplanar based on a coplanarity analysis. Commencing from the maximum LiDAR point height towards the minimum, all the LiDAR points on each height level are extracted and separated into several groups based on 2D distance. From each group, lines are extracted and a coplanar point which is the nearest to the midpoint of each line is considered as a seed point. This seed point and its neighbouring points are utilised to generate the plane equation. The plane is grown in a region growing fashion until no new points can be added. A robust rule-based tree removal method is applied subsequently to remove planar segments on trees. Four different rules are applied in this method. Finally, the boundary of each object is extracted from the segmented LiDAR point cloud. The method is evaluated with six different data sets consisting hilly and densely vegetated areas. The experimental results indicate that the proposed method offers a high building detection and roof plane extraction rates while compared to a recently proposed method.