Urban Object Extraction From Digital Surface Model and Digital Aerial Images

Grigillo, Dejan; Kanjir, Urša

doi:10.5194/isprsannals-i-3-215-2012

Cited by 50 publications

(24 citation statements)

References 11 publications

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“…NDVI has been used extensively in literature to eliminate vegetation and classify scenes [15,22,25,35]. Nevertheless, many authors [2,15] emphasised to couple it with entropy, since NDVI alone is not a promising feature to handle shadows and coloured buildings.…”

Section: Aerial Image Generated Datamentioning

confidence: 99%

“…Nevertheless, the success of this method is completely dependent on the quality of the nDSM, which is generally not available for many areas. Following the mask generation process in [15], Grigillo et al [25] eliminated the vegetation under shadows by truncating areas with low homogeneity. However, this method fails to address occlusion and works well when trees are isolated and produces inaccurate building boundary when surrounded by trees.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Automatic Building Extraction and Regularisation Technique Using LiDAR Point Cloud Data and Orthoimage

Gilani

Awrangjeb

2016

Remote Sensing

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Abstract:The development of robust and accurate methods for automatic building detection and regularisation using multisource data continues to be a challenge due to point cloud sparsity, high spectral variability, urban objects differences, surrounding complexity, and data misalignment. To address these challenges, constraints on object's size, height, area, and orientation are generally benefited which adversely affect the detection performance. Often the buildings either small in size, under shadows or partly occluded are ousted during elimination of superfluous objects. To overcome the limitations, a methodology is developed to extract and regularise the buildings using features from point cloud and orthoimagery. The building delineation process is carried out by identifying the candidate building regions and segmenting them into grids. Vegetation elimination, building detection and extraction of their partially occluded parts are achieved by synthesising the point cloud and image data. Finally, the detected buildings are regularised by exploiting the image lines in the building regularisation process. Detection and regularisation processes have been evaluated using the ISPRS benchmark and four Australian data sets which differ in point density (1 to 29 points/m 2 ), building sizes, shadows, terrain, and vegetation. Results indicate that there is 83% to 93% per-area completeness with the correctness of above 95%, demonstrating the robustness of the approach. The absence of over-and many-to-many segmentation errors in the ISPRS data set indicate that the technique has higher per-object accuracy. While compared with six existing similar methods, the proposed detection and regularisation approach performs significantly better on more complex data sets (Australian) in contrast to the ISPRS benchmark, where it does better or equal to the counterparts.

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Section: Aerial Image Generated Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Automatic Building Extraction and Regularisation Technique Using LiDAR Point Cloud Data and Orthoimage

Gilani

Awrangjeb

2016

Remote Sensing

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“…Especially morphological filtering is applied in the majority of case studies. Admittedly, it is not always employed as a form of postprocessing to improve the final output, but equally during the workflow to smooth the result of a workflow step before further processing [73,120,121,126]. When applied at the end of the workflow in case studies on road extraction, morphological filtering is often combined with skeletons to extract the vectorized centerline of the road [79,125,142,196,198].…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

“…Morphological operators are employed as a postprocessing step to smooth the contour of detected line features [195]. Morphological operators [73,75,79,115,116,120,121,126,128,146,148,196,198,199] [132] [124,125,142,144,200] Remote Sens. 2016, 8, 689…”

Section: Postprocessingmentioning

confidence: 99%

Review of Automatic Feature Extraction from High-Resolution Optical Sensor Data for UAV-Based Cadastral Mapping

et al. 2016

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Unmanned Aerial Vehicles (UAVs) have emerged as a rapid, low-cost and flexible acquisition system that appears feasible for application in cadastral mapping: high-resolution imagery, acquired using UAVs, enables a new approach for defining property boundaries. However, UAV-derived data are arguably not exploited to its full potential: based on UAV data, cadastral boundaries are visually detected and manually digitized. A workflow that automatically extracts boundary features from UAV data could increase the pace of current mapping procedures. This review introduces a workflow considered applicable for automated boundary delineation from UAV data. This is done by reviewing approaches for feature extraction from various application fields and synthesizing these into a hypothetical generalized cadastral workflow. The workflow consists of preprocessing, image segmentation, line extraction, contour generation and postprocessing. The review lists example methods per workflow step-including a description, trialed implementation, and a list of case studies applying individual methods. Furthermore, accuracy assessment methods are outlined. Advantages and drawbacks of each approach are discussed in terms of their applicability on UAV data. This review can serve as a basis for future work on the implementation of most suitable methods in a UAV-based cadastral mapping workflow.

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“…For example, Haala (1997) conducted urban land use classification using aerial images, DSM (digital surface model) and the existing 2D Geographic Information Systems (GIS). Grigillo (2012) integrated the LiDAR (Light Detection and Ranging) and aerial image to extract building and vegetation. Charaniya (2004) classified aerial LiDAR height data into roads, grass, buildings, and trees using a supervised parametric classification algorithm.…”

Section: Introductionmentioning

confidence: 99%

EXTRACTING URBAN GROUND OBJECT INFORMATION FROM IMAGES AND LiDAR DATA

Yi¹,

Zhao²,

Li³

et al. 2016

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

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ABSTRACT:To deal with the problem of urban ground object information extraction, the paper proposes an object-oriented classification method using aerial image and LiDAR data. Firstly, we select the optimal segmentation scales of different ground objects and synthesize them to get accurate object boundaries. Then, this paper uses ReliefF algorithm to select the optimal feature combination and eliminate the Hughes phenomenon. Eventually, the multiple classifier combination method is applied to get the outcome of the classification. In order to validate the feasible of this method, this paper selects two experimental regions in Stuttgart and Germany (Region A and B, covers 0.21km2 and 1.1km2 respectively). The aim of the first experiment on the Region A is to get the optimal segmentation scales and classification features. The overall accuracy of the classification reaches to 93.3%. The purpose of the experiment on region B is to validate the application-ability of this method for a large area, which is turned out to be reaches 88.4% overall accuracy. In the end of this paper, the conclusion shows that the proposed method can be performed accurately and efficiently in terms of urban ground information extraction and be of high application value.

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Urban Object Extraction From Digital Surface Model and Digital Aerial Images

Cited by 50 publications

References 11 publications

An Automatic Building Extraction and Regularisation Technique Using LiDAR Point Cloud Data and Orthoimage

An Automatic Building Extraction and Regularisation Technique Using LiDAR Point Cloud Data and Orthoimage

Review of Automatic Feature Extraction from High-Resolution Optical Sensor Data for UAV-Based Cadastral Mapping

EXTRACTING URBAN GROUND OBJECT INFORMATION FROM IMAGES AND LiDAR DATA

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