Automatic detection of residential buildings using LIDAR data and multispectral imagery

Awrangjeb, Mohammad; Ravanbakhsh, Mehdi; Fraser, Clive S.

doi:10.1016/j.isprsjprs.2010.06.001

Cited by 241 publications

(243 citation statements)

References 16 publications

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“…Several types of research on automatic building edge extraction from either LiDAR data and images have been conducted (Awrangjeb, 2016, Zhao et al, 2016, Siddiqui, et al, 2016, Yang et al, 2013, Wu et al, 2011, Awrangjeb, 2010. Wu et al (2011) fused rough building lines from a LiDAR DSM and 2D lines from oriented aerial images to extract straight 3D building lines.…”

Section: Related Workmentioning

confidence: 99%

Extraction of Building Roof Edges From Lidar Data to Optimize the Digital Surface Model for True Orthophoto Generation

Widyaningrum

Lindenbergh

Gorte

et al. 2018

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

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ABSTRACT:Various kinds of urban applications require true orthophotos. True orthophoto generation requires a DSM (Digital Surface Model) to project the photo orthogonally and minimize geometric distortion due to topographic variance. DSMs are often generated from airborne laser scan data. In urban scenes, DSM data may fail to deliver sharp and straight building roof edges. This will affect the quality of the resulting orthophotos. Therefore, it is necessary to incorporate good quality building outlines as breaklines during DSM interpolation. This study proposes a data-driven approach to construct building roof outlines from LiDAR point clouds by a workflow consisting of the following steps: given roof segments, roof boundary points are extracted using a concave hull algorithm. Straight edges may be difficult to find in complex roof configurations. Therefore, two ingredients are combined. First, RanSAC corner point preselection, and second, DBSCAN-based clustering of edge points. The method is demonstrated on an area of ±1.2 km 2 containing 42 buildings of different characteristics. A quality assessment shows that the proposed method is able to deliver 92% of building lines with acceptable geometric accuracy in comparison to a building line in the base map.

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Section: Related Workmentioning

confidence: 99%

Extraction of Building Roof Edges From Lidar Data to Optimize the Digital Surface Model for True Orthophoto Generation

Widyaningrum

Lindenbergh

Gorte

et al. 2018

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

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“…Different approaches and methods have been proposed to solve this problem. The Hough transform (HOUGH, 1962;TARSHA-KURDI et.al., 2007), slope analysis (ZEVENBERGEN and THORNE, 1987), and Normalized Difference Vegetation Index (NDVI) (ROTTENSTEINER et al 2007;DEMIR et al, 2009;AWRANGJEB et al, 2010) are examples of such methods. In addition, different classification methods, such as ISO data classification (RICHARDS, 1999;HAALA and BRENNER, 1999), have been used to improve classification results for building extraction.…”

Section: Introductionmentioning

confidence: 99%

“…Beger et al (2011) used the object-oriented image analysis method with a fusion of high-resolution aerial imagery and LiDAR data for automated railroad center line reconstruction. Awrangjeb et al (2010) reported completeness results for object-based (97%) and pixel-based (78%) methods using LiDAR data and multispectral imagery. Vosselman (2000), Zhang et al (2003), Sithole (2005), Zeng (2008), and Sampath and Shan (2010) applied different slope-based filters to separate non-ground objects using LiDAR point clouds.…”

Section: Introductionmentioning

confidence: 99%

Automatic building extraction using LiDAR and aerial photographs

Uzar

Yastikli

2013

Bol. Ciênc. Geod.

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This paper presents an automatic building extraction approach using LiDAR data and aerial photographs from a multi-sensor system positioned at the same platform. The automatic building extraction approach consists of segmentation, analysis and classification steps based on object-based image analysis. The chessboard, contrast split and multi-resolution segmentation methods were used in the segmentation step. The determined object primitives in segmentation, such as scale parameter, shape, completeness, brightness, and statistical parameters, were used to determine threshold values for classification in the analysis step. The rule-based classification was carried out with defined decision rules based on determined object primitives and fuzzy rules. In this study, hierarchical classification was preferred. First, the vegetation and ground classes were generated; the building class was then extracted. The NDVI, slope and Hough images were generated and used to avoid confusing the building class with other classes. The intensity images generated from the LiDAR data and morphological operations were utilized to improve the accuracy of the building class. The proposed approach achieved an overall accuracy of approximately 93% for the target class in a suburban neighborhood, which was the study area. Moreover, completeness (96.73%) and correctness (95.02%) analyses were performed by comparing the automatically extracted buildings and reference data. Keywords: LiDAR; Building Extraction; Hough; NDVI; Segmentation; Classification.Automatic building extraction using LiDAR and aerial photographs.Bol. Ciênc. Geod., sec. Artigos, Curitiba, v. 19, n o 2, p.153-171, abr-jun, 2013.1 5 4 RESUMO Este artigo apresenta uma abordagem para a extração automática de edificações usando dados LiDAR e fotografias aéreas de um sistema com múltiplos sensores posicionados na mesma plataforma. A abordagem de extração automática de edificações é composta por etapas de segmentação, análise e classificação, baseadas em análise de imagens com base em objetos. Na etapa de segmentação foram usados os métodos Chessboard, fatiamento do constraste e multirresolução. As primitivas de segmentação, como escala, forma, integridade, brilho e parâmetros estatísticos, foram usadas para determinar os valores-limite para a classificação na etapa de análise. A classificação baseada em regras foi realizada com regras de decisão definidos com base nas primitivas de determinado objeto e regras fuzzy. Neste estudo, preferiu-se a classificação hierárquica. Primeiramente, foram geradas as classes de vegetação e solo e então foi extraída a classe de edifícações. O NDVI, declividade, e as imagens Hough foram gerados e usados para evitar confundir a classe edificações com outras classes. As imagens de intensidade geradas a partir dos dados LiDAR e operações morfológicas foram utilizados para melhorar a precisão da classe de edifícações. A abordagem proposta alcançou uma exatidão de aproximadamente 93% para a classe alvo em um bairro suburbano, que era a área de ...

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“…In the literature several papers deal with such topics. Some contributions considered as input data only images (Zebelin et al, 2006), others considered range information (Habib et al, 2009;Sampath and Shan, 2009;Pu and Vosselmann, 2009) and a growing number of papers rely on the integration of different data sources (Demir et al, 2009) and in particular from both range and image data (Alshawabkeh, 2006;Awrangjeb, et al 2010;Habib et al, 2010). The single-technique approaches usually provide good results in very specific applications, while they are unable to be adapted to operative conditions far from their original use.…”

Section: Introductionmentioning

confidence: 99%

Range and Image Data Integration for Man-Made Object Reconstruction

Nex

Remondino

2013

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

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ABSTRACT:The extraction of information from image and range data is one of the main research topics. In literature, several papers dealing with this topic has been already presented. In particular, several authors have suggested an integrated use of both range and image information in order to increase the reliability and the completeness of the results exploiting their complementary nature. In this paper, an integration between range and image data for the geometric reconstruction of man-made object is presented. In particular, the focus of this paper is on the edge extraction procedure performed in an integrated way exploiting the information provided by both range and image data. Both terrestrial and aerial applications have been analysed for the façade extraction in terrestrial acquisitions and the roof outline extraction from aerial data. The algorithm and the achieved results are described and discussed in detail.

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Automatic detection of residential buildings using LIDAR data and multispectral imagery

Cited by 241 publications

References 16 publications

Extraction of Building Roof Edges From Lidar Data to Optimize the Digital Surface Model for True Orthophoto Generation

Extraction of Building Roof Edges From Lidar Data to Optimize the Digital Surface Model for True Orthophoto Generation

Automatic building extraction using LiDAR and aerial photographs

Range and Image Data Integration for Man-Made Object Reconstruction

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