Context-Based Urban Terrain Reconstruction From Uav-Videos for Geoinformation Applications

Bulatov, Dimitri; Solbrig, Peter; Gross, Hermann; Wernerus, Peter; Repasi, Endre; Heipke, Christian

doi:10.5194/isprsarchives-xxxviii-1-c22-75-2011

Cited by 32 publications

(31 citation statements)

References 18 publications

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“…After image matching and the generation of a digital terrain model (DTM), a rule-based classification of buildings and vegetation is carried out, and the boundary polygons of the buildings are extracted. Optionally, the polygons can be regularised (Bulatov et al, 2011).…”

Section: Bulatov Fraunhofer Institute Ettlingen Germany (Fie)mentioning

confidence: 99%

See 1 more Smart Citation

The Isprs Benchmark on Urban Object Classification and 3d Building Reconstruction

Rottensteiner¹,

Sohn

Jung

et al. 2012

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

396

273

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ABSTRACT:For more than two decades, many efforts have been made to develop methods for extracting urban objects from data acquired by airborne sensors. In order to make the results of such algorithms more comparable, benchmarking data sets are of paramount importance. Such a data set, consisting of airborne image and laserscanner data, has been made available to the scientific community. Researchers were encouraged to submit results of urban object detection and 3D building reconstruction, which were evaluated based on reference data. This paper presents the outcomes of the evaluation for building detection, tree detection, and 3D building reconstruction. The results achieved by different methods are compared and analysed to identify promising strategies for automatic urban object extraction from current airborne sensor data, but also common problems of state-of-the-art methods.

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Section: Bulatov Fraunhofer Institute Ettlingen Germany (Fie)mentioning

confidence: 99%

“…Section 3.1) a detailed roof analysis is performed, using a RANSAC based approach for the initial clustering of normal vectors (Bulatov et al, 2011).…”

Section: Bulatov Fraunhofer Institute Ettlingen Germany (Fie)mentioning

confidence: 99%

The Isprs Benchmark on Urban Object Classification and 3d Building Reconstruction

Rottensteiner¹,

Sohn

Jung

et al. 2012

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

396

273

View full text Add to dashboard Cite

show abstract

“…Therefore, this study investigates the effect of number International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-2/W2, ISPRS 8th 3DGeoInfo Conference & WG II/2 Workshop, 27 -29 November 2013, Istanbul, Turkey GCPs in image processing based on block configuration. A proposed image registration introduced by (Bulatov et al, 2011) was used in this study; co-registration process and georeferencing process in DO.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation on Different Number of Ground Control Points in Unmanned Aerial Vehicle Photogrammetric Block

Tahar

2013

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

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ABSTRACT:This paper presents a novel method of unmanned aerial vehicle image processing using photogrammetric technique. The study area consist different slope class which involves undulating area around the Skudai area, Malaysia. All photogrammetric methods were applied in this study including selection of test area, flight planning, camera calibration and image processing orientation. A new ground control point configurations is introduced in this study as a generic approach in unmanned aerial vehicle photogrammetric block. These configurations are used to determine the best photogrammetric results based on number of ground control points in the photogrammetric block during image processing. The objective of this study is to determine the best configuration for photogrammetric block in order to produce the best photogrammetric products. Photogrammetric image processing involves two main orientations which are known as interior orientation and exterior orientation. Interior orientation involves the parameters of camera in order to calibrate the image in the same position as during the image acquisition. Exterior orientation involves the measurement of tie points to tie up all images in the strips until photogrammetric block. In this study, six ground control point configurations were tested to determine the best photogrammetric results. In this study, two main photogrammetric results were produced namely digital orthophoto and digital elevation model. The verification results show that all configurations recorded coefficient percentage of more than 97 % accuracy for the six configurations. The validation results conclude that ground control point plays an important role in photogrammetric block to acquire the accurate photogrammetric results. In this study eight and nine ground control point configurations are the best configurations among the others. Qualitatively, vector plot error for easting and northing coordinates can be viewed graphically to distinguish the error and direction for all configurations which has been proposed in this study.

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“…Bulatov et al (2012) used an imagebased DSM to compute normal vectors for each pixel, while Omidalizarandi and Saadatseresgt (2013) performed region growing on image based point clouds to form planar segments. However, it was found that errors from planar segmentation can arise at the location of the planar boundaries (Omidalizarandi and Saadatseresgt, 2013).…”

Section: Related Workmentioning

confidence: 99%

Automatic 3d Building Reconstruction From a Dense Image Matching Dataset

McClune¹,

Mills²,

Miller³

et al. 2016

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

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ABSTRACT:Over the last 20 years the demand for three dimensional (3D) building models has resulted in a vast amount of research being conducted in attempts to automate the extraction and reconstruction of models from airborne sensors. Recent results have shown that current methods tend to favour planar fitting procedures from lidar data, which are able to successfully reconstruct simple roof structures automatically but fail to reconstruct more complex structures or roofs with small artefacts. Current methods have also not fully explored the potential of recent developments in digital photogrammetry. Large format digital aerial cameras can now capture imagery with increased overlap and a higher spatial resolution, increasing the number of pixel correspondences between images. Every pixel in each stereo pair can also now be matched using per-pixel algorithms, which has given rise to the approach known as dense image matching. This paper presents an approach to 3D building reconstruction to try and overcome some of the limitations of planar fitting procedures. Roof vertices, extracted from true-orthophotos using edge detection, are refined and converted to roof corner points. By determining the connection between extracted corner points, a roof plane can be defined as a closed-cycle of points. Presented results demonstrate the potential of this method for the reconstruction of complex 3D building models at CityGML LoD2 specification.

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Context-Based Urban Terrain Reconstruction From Uav-Videos for Geoinformation Applications

Cited by 32 publications

References 18 publications

The Isprs Benchmark on Urban Object Classification and 3d Building Reconstruction

The Isprs Benchmark on Urban Object Classification and 3d Building Reconstruction

An Evaluation on Different Number of Ground Control Points in Unmanned Aerial Vehicle Photogrammetric Block

Automatic 3d Building Reconstruction From a Dense Image Matching Dataset

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