An Automatic and Threshold-Free Performance Evaluation System for Building Extraction Techniques From Airborne LIDAR Data

Awrangjeb, Mohammad; Fraser, Clive S.

doi:10.1109/jstars.2014.2318694

Cited by 78 publications

(84 citation statements)

References 46 publications

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“…The objective evaluation follows a modified version of a previous automatic and threshold-free evaluation system (Awrangjeb and Fraser, 2014a).…”

Section: Resultsmentioning

confidence: 99%

“…In order to assess the performance of the proposed method, the automatic and threshold-free evaluation system (Awrangjeb and Fraser, 2014a) has been employed. It offers more robust objectbased evaluation of building extraction techniques than the thresholdbased system (Rutzinger et al, 2009) adopted by the ISPRS benchmark system.…”

Section: Evaluation Systemmentioning

confidence: 99%

“…Currently, there are no unanimous evaluation system and standard guidelines available to measure the performance of building extraction techniques (Rutzinger et al, 2009, Awrangjeb andFraser, 2014a). Indeed, evaluation results are often missing from published accounts of building detection; the use of 1-2 evaluation indices only has characterised many studies (Demir et al, 2009, Awrangjeb et al, 2010.…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, evaluation results are often missing from published accounts of building detection; the use of 1-2 evaluation indices only has characterised many studies (Demir et al, 2009, Awrangjeb et al, 2010. This research aims to develop, firstly, a mechanism to effectively integrate features extracted from Li-DAR and multispectral imagery to increase building detection performance and, secondly, a comprehensive evaluation of the proposed method using an automatic performance evaluation system using 15 evaluation indices (Awrangjeb and Fraser, 2014a).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fusion of Lidar Data and Multispectral Imagery for Effective Building Detection Based on Graph and Connected Component Analysis

Gilani

Awrangjeb

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Building detection in complex scenes is a non-trivial exercise due to building shape variability, irregular terrain, shadows, and occlusion by highly dense vegetation. In this research, we present a graph based algorithm, which combines multispectral imagery and airborne LiDAR information to completely delineate the building boundaries in urban and densely vegetated area. In the first phase, LiDAR data is divided into two groups: ground and non-ground data, using ground height from a bare-earth DEM. A mask, known as the primary building mask, is generated from the non-ground LiDAR points where the black region represents the elevated area (buildings and trees), while the white region describes the ground (earth). The second phase begins with the process of Connected Component Analysis (CCA) where the number of objects present in the test scene are identified followed by initial boundary detection and labelling. Additionally, a graph from the connected components is generated, where each black pixel corresponds to a node. An edge of a unit distance is defined between a black pixel and a neighbouring black pixel, if any. An edge does not exist from a black pixel to a neighbouring white pixel, if any. This phenomenon produces a disconnected components graph, where each component represents a prospective building or a dense vegetation (a contiguous block of black pixels from the primary mask). In the third phase, a clustering process clusters the segmented lines, extracted from multispectral imagery, around the graph components, if possible. In the fourth step, NDVI, image entropy, and LiDAR data are utilised to discriminate between vegetation, buildings, and isolated building's occluded parts. Finally, the initially extracted building boundary is extended pixel-wise using NDVI, entropy, and LiDAR data to completely delineate the building and to maximise the boundary reach towards building edges. The proposed technique is evaluated using two Australian data sets: Aitkenvale and Hervey Bay, for object-based and pixel-based completeness, correctness, and quality. The proposed technique detects buildings larger than 50 m 2 and 10 m 2 in the Aitkenvale site with 100% and 91% accuracy, respectively, while in the Hervey Bay site it performs better with 100% accuracy for buildings larger than 10 m 2 in area.

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“…The objective evaluation follows a modified version of a previous automatic and threshold-free evaluation system (Awrangjeb and Fraser, 2014a).…”

Section: Resultsmentioning

confidence: 99%

Section: Evaluation Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fusion of Lidar Data and Multispectral Imagery for Effective Building Detection Based on Graph and Connected Component Analysis

Gilani

Awrangjeb

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…The performance of the building detection step was evaluated using the object-based completeness and correctness measures, as well as the number of split and merge operations (Awrangjeb and Fraser, 2014a). Split operations are required during evaluation due to under-segmentation errors when two or more buildings are detected as one single building.…”

Section: Building Detection Resultsmentioning

confidence: 99%

Building Change Detection From Lidar Point Cloud Data Based on Connected Component Analysis

Awrangjeb

Fraser

2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT: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 undersegmentation 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.

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Clustering Quality Measures for Point Cloud Segmentation Tasks

Walczak

Wojciechowski

2018

Lecture Notes in Computer Science

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An Automatic and Threshold-Free Performance Evaluation System for Building Extraction Techniques From Airborne LIDAR Data

Cited by 78 publications

References 46 publications

Fusion of Lidar Data and Multispectral Imagery for Effective Building Detection Based on Graph and Connected Component Analysis

Fusion of Lidar Data and Multispectral Imagery for Effective Building Detection Based on Graph and Connected Component Analysis

Building Change Detection From Lidar Point Cloud Data Based on Connected Component Analysis

Clustering Quality Measures for Point Cloud Segmentation Tasks

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