A New Region Growing-Based Method for Road Network Extraction and Its Application on Different Resolution SAR Images

Lu, Pingping; Du, Kangning; Yu, Weidong; Wang, Robert; Deng, Yunkai; Balz, Timo

doi:10.1109/jstars.2014.2340394

Cited by 36 publications

(20 citation statements)

References 18 publications

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“…The remaining road strings, represented by their centre lines, are connected to a road network. Similar methods can be found in other literatures (Baumgartner, 1997;Baumgartner, 1999;Lu, 2014). Zhao etc (2011) proposed a classification based method, where LIDAR data is used for rapid road network extraction.…”

Section: Introductionmentioning

confidence: 74%

Road Network Extraction From DSM by Mathematical Morphology and Reasoning

Li¹,

Wu²,

Zhu³

et al. 2016

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

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ABSTRACT:The objective of this research is the automatic extraction of the road network in a scene of the urban area from a high resolution digital surface model (DSM). Automatic road extraction and modeling from remote sensed data has been studied for more than one decade. The methods vary greatly due to the differences of data types, regions, resolutions et al. An advanced automatic road network extraction scheme is proposed to address the issues of tedium steps on segmentation, recognition and grouping. It is on the basis of a geometric road model which describes a multiple-level structure. The 0-dimension element is intersection. The 1-dimension elements are central line and side. The 2-dimension element is plane, which is generated from the 1-dimension elements. The key feature of the presented approach is the cross validation for the three road elements which goes through the entire procedure of their extraction. The advantage of our model and method is that linear elements of the road can be derived directly, without any complex, non-robust connection hypothesis. An example of Japanese scene is presented to display the procedure and the performance of the approach.

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Section: Introductionmentioning

confidence: 74%

Road Network Extraction From DSM by Mathematical Morphology and Reasoning

Li¹,

Wu²,

Zhu³

et al. 2016

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

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“…We note that [16] suggested techniques to reduce the false-alarm rate that rely on the connectivity constraint of the bridge with road. Moreover, many algorithms have been developed for road extracted in SAR images [17][18][19]. In the bridge-detection stage, these methods can be used as a part of our approach to identify rivers/bridges.…”

Section: Resultsmentioning

confidence: 99%

Bridge Detection in High-Resolution X-Band Sar Images by Combined Statistical and Topological Approach

Yang¹,

Zhihua²

2017

PIER M

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This article takes account of the radiation feature of rivers in high-resolution airborne Xband synthetic aperture radar (SAR) images and proposes a novel automatic approach to detect highway bridges by combining statistical and topology features. The proposed method consists of two steps. In the river-extraction stage, the classification techniques are applied to water extraction according to the statistical and gray-leveled features. In the bridge-extraction stage, bridges are then detected in this binary image by using a topology-based approach. Experiments, undertaken on high-resolution X-band SAR images, confirm that the proposed method can be implemented with high-precision highway-bridge extraction, feature analysis, and bridge recognition.

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“…In [18], an accurate road centerline extraction method from HSR multispectral images is presented, which integrates tensor voting, principal curves, and the geodesic method to cope with complicated road shapes. In [19], road network extraction is investigated by using synthetic aperture radar (SAR) images, and a new method is developed based on the region growing to quickly extract the road network, which is suitable for different resolution SAR images.…”

Section: Fine Structure Extractionmentioning

confidence: 99%

Foreword to the Special Issue on Pattern Recognition in Remote Sensing

Michaelsen

et al. 2014

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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A New Region Growing-Based Method for Road Network Extraction and Its Application on Different Resolution SAR Images

Cited by 36 publications

References 18 publications

Road Network Extraction From DSM by Mathematical Morphology and Reasoning

Road Network Extraction From DSM by Mathematical Morphology and Reasoning

Bridge Detection in High-Resolution X-Band Sar Images by Combined Statistical and Topological Approach

Foreword to the Special Issue on Pattern Recognition in Remote Sensing

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