Automated Road Extraction from High Resolution Multispectral Imagery

Doucette, Peter; Agouris, Peggy; Stefanidis, Anthony

doi:10.14358/pers.70.12.1405

Cited by 56 publications

(59 citation statements)

References 9 publications

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“…Ameri and Valadan [25] motivated by research of Doucette et al [26], introduced a road vectorization method based on dynamic clustering. This approach utilizes a swarm-based optimization technique that relies on a modified cost function.…”

Section: Introductionmentioning

confidence: 99%

Road Network Extraction from VHR Satellite Images Using Context Aware Object Feature Integration and Tensor Voting

et al. 2016

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Road networks are very important features in geospatial databases. Even though high-resolution optical satellite images have already been acquired for more than a decade, tools for automated extraction of road networks from these images are still rare. One consequence of this is the need for manual interaction which, in turn, is time and cost intensive. In this paper, a multi-stage approach is proposed which integrates structural, spectral, textural, as well as contextual information of objects to extract road networks from very high resolution satellite images. Highlights of the approach are a novel linearity index employed for the discrimination of elongated road segments from other objects and customized tensor voting which is utilized to fill missing parts of the network. Experiments are carried out with different datasets. Comparison of the achieved results with the results of seven state-of-the-art methods demonstrated the efficiency of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Road Network Extraction from VHR Satellite Images Using Context Aware Object Feature Integration and Tensor Voting

et al. 2016

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“…Our algorithm is similar to the algorithms that use Delaunay (1934) triangulation andVoronoi (1908) The terms central line, skeleton and medial axis are used often in literature to describe related things (Greenspan et al, 2001;Palagyi et al, 2001). The terms central line and medial axis appear most often concerning thinning of the object on the surface such as road (Doucette et al, 2004). The term skeleton is more associated to volumetric data such as 3D trees, sculptures, body (Tagliasacchi et al, 2009;Livny et al, 2010;Bucksch and Fleck, 2011;Kasap and Magnenat-Thalmann, 2011).…”

Section: Related Workmentioning

confidence: 99%

3d Central Line Extraction of Fossil Oyster Shells

Djuricic

Puttonen

Harzhauser

et al. 2016

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

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ABSTRACT:Photogrammetry provides a powerful tool to digitally document protected, inaccessible, and rare fossils. This saves manpower in relation to current documentation practice and makes the fragile specimens more available for paleontological analysis and public education. In this study, high resolution orthophoto (0.5 mm) and digital surface models (1 mm) are used to define fossil boundaries that are then used as an input to automatically extract fossil length information via central lines. In general, central lines are widely used in geosciences as they ease observation, monitoring and evaluation of object dimensions. Here, the 3D central lines are used in a novel paleontological context to study fossilized oyster shells with photogrammetric and LiDAR-obtained 3D point cloud data. 3D central lines of 1121 Crassostrea gryphoides oysters of various shapes and sizes were computed in the study. Central line calculation included: i) Delaunay triangulation between the fossil shell boundary points and formation of the Voronoi diagram; ii) extraction of Voronoi vertices and construction of a connected graph tree from them; iii) reduction of the graph to the longest possible central line via Dijkstra's algorithm; iv) extension of longest central line to the shell boundary and smoothing by an adjustment of cubic spline curve; and v) integration of the central line into the corresponding 3D point cloud. The resulting longest path estimate for the 3D central line is a size parameter that can be applied in oyster shell age determination both in paleontological and biological applications. Our investigation evaluates ability and performance of the central line method to measure shell sizes accurately by comparing automatically extracted central lines with manually collected reference data used in paleontological analysis. Our results show that the automatically obtained central line length overestimated the manually collected reference by 1.5% in the test set, which is deemed sufficient for the selected paleontological application, namely shell age determination.

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“…The road extraction using imaging radar is still an open scientific question, and existing applications express differentiated solutions. This process is considered a major component in cartography [Doucette et al, 2004]. Some roads extractors require initial points to start the extraction process.…”

Section: Motivationmentioning

confidence: 99%

Roads Centre-Axis Extraction in Airborne Sar Images: An Approach Based on Active Contour Model With the Use of Semi-Automatic Seeding

Lotte

Sant’Anna

Almeida

2013

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

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ABSTRACT:Research works dealing with computational methods for roads extraction have considerably increased in the latest two decades. This procedure is usually performed on optical or microwave sensors (radar) imagery. Radar images offer advantages when compared to optical ones, for they allow the acquisition of scenes regardless of atmospheric and illumination conditions, besides the possibility of surveying regions where the terrain is hidden by the vegetation canopy, among others. The cartographic mapping based on these images is often manually accomplished, requiring considerable time and effort from the human interpreter. Maps for detecting new roads or updating the existing roads network are among the most important cartographic products to date. There are currently many studies involving the extraction of roads by means of automatic or semi-automatic approaches. Each of them presents different solutions for different problems, making this task a scientific issue still open. One of the preliminary steps for roads extraction can be the seeding of points belonging to roads, what can be done using different methods with diverse levels of automation. The identified seed points are interpolated to form the initial road network, and are hence used as an input for an extraction method properly speaking. The present work introduces an innovative hybrid method for the extraction of roads centre-axis in a synthetic aperture radar (SAR) airborne image. Initially, candidate points are fully automatically seeded using Self-Organizing Maps (SOM), followed by a pruning process based on specific metrics. The centre-axis are then detected by an open-curve active contour model (snakes). The obtained results were evaluated as to their quality with respect to completeness, correctness and redundancy.

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Automated Road Extraction from High Resolution Multispectral Imagery

Cited by 56 publications

References 9 publications

Road Network Extraction from VHR Satellite Images Using Context Aware Object Feature Integration and Tensor Voting

Road Network Extraction from VHR Satellite Images Using Context Aware Object Feature Integration and Tensor Voting

3d Central Line Extraction of Fossil Oyster Shells

Roads Centre-Axis Extraction in Airborne Sar Images: An Approach Based on Active Contour Model With the Use of Semi-Automatic Seeding

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