Airborne photogrammetry and lidar for DSM extraction and 3D change detection over an urban area – a comparative study

Stal, Cornelis; Tack, Frederik; Maeyer, Philippe De; Wulf, Alain De; Goossens, Rudi

doi:10.1080/01431161.2012.717183

Cited by 97 publications

(65 citation statements)

References 26 publications

(24 reference statements)

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“…Thus, the roughness index can be applied to differentiate false alarms caused by trees. In the work of Pang et al [35] and Stal et al [45], a plane filter and roughness index were employed to remove the vegetated areas, respectively. However, since building roofs may be more complex, especially for a skyscraper, it is almost impossible to fit planes or calculate the roughness as the index.…”

Section: Positive Building Change Results Refinementmentioning

confidence: 99%

“…However, there is limited research focused on building change detection using old aerial images and new LiDAR data. Relevant research can be found in Stal et al [45], who employed airborne photogrammetry and LiDAR to extract DSMs for detecting the 3D changes over an urban area. Their major focus was to compare the performance and feasibility of using airborne photogrammetry and LiDAR techniques for 3D surface modeling, and extracting 3D building changes information were only performed using DSM differencing.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Building Change Detection Using Old Aerial Images and New LiDAR Data

Zhang

Qin

et al. 2016

Remote Sensing

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Building change detection is important for urban area monitoring, disaster assessment and updating geo-database. 3D information derived from image dense matching or airborne light detection and ranging (LiDAR) is very effective for building change detection. However, combining 3D data from different sources is challenging, and so far few studies have focused on building change detection using both images and LiDAR data. This study proposes an automatic method to detect building changes in urban areas using aerial images and LiDAR data. First, dense image matching is carried out to obtain dense point clouds and then co-registered LiDAR point clouds using the iterative closest point (ICP) algorithm. The registered point clouds are further resampled to a raster DSM (Digital Surface Models). In a second step, height difference and grey-scale similarity are calculated as change indicators and the graph cuts method is employed to determine changes considering the contexture information. Finally, the detected results are refined by removing the non-building changes, in which a novel method based on variance of normal direction of LiDAR points is proposed to remove vegetated areas for positive building changes (newly building or taller) and nEGI (normalized Excessive Green Index) is used for negative building changes (demolish building or lower). To evaluate the proposed method, a test area covering approximately 2.1 km 2 and consisting of many different types of buildings is used for the experiment. Results indicate 93% completeness with correctness of 90.2% for positive changes, while 94% completeness with correctness of 94.1% for negative changes, which demonstrate the promising performance of the proposed method.

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Section: Positive Building Change Results Refinementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Building Change Detection Using Old Aerial Images and New LiDAR Data

Zhang

Qin

et al. 2016

Remote Sensing

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“…For spatiotemporal modeling, by modeling and comparing a cityscape at different time snapshots, a final 3D building change model map, which shows the destructed and newly constructed buildings, can be acquired. Based on the 3D change map, the volume and surface of the building changes were quantified [40]. In addition, a spatiotemporal GIS model was built to study changes in the physical form of cities by visualizing the dynamic transition of the 3DCM over time [41].…”

Section: Discussion Of Studies Of Urban Issues Underpinned By Simplifmentioning

confidence: 99%

An Integrated Simplification Approach for 3D Buildings with Sloped and Flat Roofs

Xie

Feng

2016

IJGI

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Simplification of three-dimensional (3D) buildings is critical to improve the efficiency of visualizing urban environments while ensuring realistic urban scenes. Moreover, it underpins the construction of multi-scale 3D city models (3DCMs) which could be applied to study various urban issues. In this paper, we design a generic yet effective approach for simplifying 3D buildings. Instead of relying on both semantic information and geometric information, our approach is based solely on geometric information as many 3D buildings still do not include semantic information. In addition, it provides an integrated means to treat 3D buildings with either sloped or flat roofs. The two case studies, one exploring simplification of individual 3D buildings at varying levels of complexity while the other, investigating the multi-scale simplification of a cityscape, show the effectiveness of our approach.

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“…Height change reflects the main change of buildings and elevation information is an important index for extracting building change information (Stal et al, 2013). High precision DSM can be generated by LiDAR data and building height change can be obtained through DSM differencing, then new or demolished buildings can be detected (Alobeid et at., 2011).…”

Section: Methodsmentioning

confidence: 99%

BUILDING CHANGE DETECTION BY COMBINING LiDAR DATA AND ORTHO IMAGE

Peng

Zhang

2016

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

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ABSTRACT:The elevation information is not considered in the traditional building change detection methods. This paper presents an algorithm of combining LiDAR data and ortho image for 3D building change detection. The advantages of the proposed approach lie in the fusion of the height and spectral information by thematic segmentation. Furthermore, the proposed method also combines the advantages of pixel-level and object-level change detection by image differencing and object analysis. Firstly, two periods of LiDAR data are filtered and interpolated to generate their corresponding DSMs. Secondly, a binary image of the changed areas is generated by means of differencing and filtering the two DSMs, and then thematic layer is generated and projected onto the DSMs and DOMs. Thirdly, geometric and spectral features of the changed area are calculated, which is followed by decision tree classification for the purpose of extracting the changed building areas. Finally, the statistics of the elevation and area change information as well as the change type of the changed buildings are done for building change analysis. Experimental results show that the completeness and correctness of building change detection are close to 81.8% and 85.7% respectively when the building area is larger than 80 2 m , which are increased about 10% when compared with using ortho image alone.

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Airborne photogrammetry and lidar for DSM extraction and 3D change detection over an urban area – a comparative study

Cited by 97 publications

References 26 publications

Building Change Detection Using Old Aerial Images and New LiDAR Data

Building Change Detection Using Old Aerial Images and New LiDAR Data

An Integrated Simplification Approach for 3D Buildings with Sloped and Flat Roofs

BUILDING CHANGE DETECTION BY COMBINING LiDAR DATA AND ORTHO IMAGE

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