Improvement of road extraction in high resolution SAR data by a context-based approach

Amberg, V.; Coulon, Martial; Marthon, Philippe; Spigai, Marc

doi:10.1109/igarss.2005.1526218

Cited by 10 publications

(9 citation statements)

References 4 publications

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“…(5)The alignment must be partly in the "road region" and it means that T5 percent of the alignment must be in the "road region". (The T5 can be set between 50~100)…”

Section: Scene Context Guided Perceptual Groupingmentioning

confidence: 99%

“…Amberg etc. [5] post process the road detection result on the basis of contextual information, but did not give final result and analysis (At the time of their paper, the automatic integration of contextual information in their road process is under investigation). Karin Hedman etc.…”

Section: Introductionmentioning

confidence: 98%

“…Only recently, in light of the advent of new satellites carrying high-resolution SAR sensors (TerraSAR-X, RADARSAT-2 and COSMO-SkeyMet), works about road network extraction from high-resolution SAR data have started [1][2][3][4][5][6][7] Due to its weather independence and the ability to operate during both day and night, SAR has indeed some advantages over VHR optical data. However, for SAR data based road network detection, there are many difficulties.…”

Section: Introductionmentioning

confidence: 99%

“…The characteristics of roads in one single SAR scene can be classified in five groups [10]: geometrical, radiometrical, topological, functional and contextual characteristics. The geometrical and radiometrical characteristics have been widely used in road segments detection step [1][2][3][4][5][6][7] [11]. Some works using topological characteristics have been proposed on network reconstruction after road segments extraction in SAR imagery.…”

Section: Introductionmentioning

confidence: 99%

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SAR image road network extraction with scene context priming

Cao

Tang

2011

2011 IEEE International Geoscience and Remote Sensing Symposium

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In this paper, a novel method of road network extraction with scene context priming for Synthetic Aperture Radar (SAR) images is proposed. In this method, the scene contextual information of each site, that this site is in roadexist scene or road-not-exist scene, is got by an SVM classifier. Then this information is used for guiding perceptual grouping of the lines and curves got by a line extractor. The output of perceptual grouping is further refined by Markov Random Field (MRF) based global optimization method to get the final road network. The performance of the method is tested on a high resolution TerraSAR-X image of urban scene.

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“…(5)The alignment must be partly in the "road region" and it means that T5 percent of the alignment must be in the "road region". (The T5 can be set between 50~100)…”

Section: Scene Context Guided Perceptual Groupingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

SAR image road network extraction with scene context priming

Cao

Tang

2011

2011 IEEE International Geoscience and Remote Sensing Symposium

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“…But we can also see that the method can not handle complex road intersections and roads in build-up areas very well. This may be improved by employing the junction-aware MRF model [23] and introducing context-based information [24,25] which will be considered in further study. There are many approaches to evaluate the results of automatic road extraction [26], and we choose the common three indexes [27] to evaluate our method, they are defined as (14).…”

Section: Road Extraction Using Mlfdmentioning

confidence: 99%

A novel linear feature detector for SAR images

Liao

Yang

et al. 2012

EURASIP J. Adv. Signal Process.

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A new linear feature detector for synthetic aperture radar (SAR) images is presented in this article by embedding a three-region filter into the wedgelet analysis framework. One of its main features is that it can detect linear features with a range of varying widths and orientations in the same image by changing the direction and size of the detector mask within a multiscale framework. In addition, this detector takes into account both statistical and geometrical characteristics to detect line segments directly instead of detecting target pixels. To show its effectiveness, the detector is applied to extract one of the most important linear features: roads. Results and comparisons with several multiscale analysis techniques as well as ratio correlation detector on DLR E-SAR images reveal its advantages.

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