Improving Urban Road Extraction in High-Resolution Images Exploiting Directional Filtering, Perceptual Grouping, and Simple Topological Concepts

Gamba, Paolo; Dell’Acqua, Fabio; Lisini, Gianni

doi:10.1109/lgrs.2006.873875

Cited by 113 publications

(51 citation statements)

References 13 publications

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“…A solution to join these line segments is suggested by Dell'Acqua et al, 2002, Gamba et al, 2006, Mohan and Nevatia, 1992, Hu and Tao, 2007, Amini et al, 2002 technique called Perceptual Grouping and perhaps an alternative in future research. The experiment conducted with the synthetic image shows the efficiency of the Snakes method when the seed points are marked near of the road, mostly, converging exactly to the central axis of the roads.…”

Section: Resultsmentioning

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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Section: Resultsmentioning

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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“…Dell'Acqua and Gamba [5] develop an algorithm using fuzzy Hough transform to extract roads. Gamba et al [6] present a study for urban road extraction by utilising proposed algorithm in [5], adaptive directional filtering and perceptual grouping. A new method for a feature based supervised classification is presented by Borghys et al [7].…”

Section: Introductionmentioning

confidence: 99%

Effect of Threshold Values Used for Road Segments Detection in SAR Images on Road Network Generation

Açar¹,

Bayir²

2017

ijacsa

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Abstract-In this study, the effect of threshold values used for road segments detection in synthetic aperture radar (SAR) images of road network generation is examined. A three-phase method is applied as follows: image smoothing, road segments detection and irrelevant segments removal. Threshold values used in road segment detection phase are evaluated for four different situations and results are compared. The software is developed to apply and test all situations. Two different synthetic aperture radar images are used in experimental studies.

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“…The disadvantage of GMTI however, is the necessity of a moving target, should the target stop or be obstructed in any way from the sensors, the tracker will lose the target for the duration of the obstruction [2]. Many of the currently available algorithms rely on information from preexisting road maps[ [1], [3], [5], [7]], however in many scenarios the availability of this a-priori information is limited and inaccurate. In additional situations there is no existence of road maps, such as in times of conflict in desert regions.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, there is a lack of a quantifiable measure of the accuracy of the extracted road estimates. Several available algorithms use a "completeness" and "correctness" measure which is a comparison of the extracted road network and the actual network [ [7], [8], [9], [11]], however as previously stated in many situations there is no available truth so these methods are not relevant.…”

Section: Introductionmentioning

confidence: 99%

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Synthesis of Road Networks by Data Conflation

Singh¹

2014

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Improving Urban Road Extraction in High-Resolution Images Exploiting Directional Filtering, Perceptual Grouping, and Simple Topological Concepts

Cited by 113 publications

References 13 publications

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

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

Effect of Threshold Values Used for Road Segments Detection in SAR Images on Road Network Generation

Synthesis of Road Networks by Data Conflation

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