&lt;title&gt;Road detection in spaceborne SAR images using genetic algorithm&lt;/title&gt;

Jeon, Byoung-Ki; Jang, Jum Suk; Hong, Ki-Sang

doi:10.1117/12.396324

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…In this pruning phase, the final road results of the beamlet are offered. Perceptual organization, which is known to assess the structural relationships of various primitive elements, has been widely used in computer vision [22,23]. In this study, the contextual relationships between road segments, such as the intersection angles, curvatures, endpoint distances and proximities, provide global optimization with effective prior constraints for road extraction from SAR images.…”

Section: Prior Constraints Under Beamlet Analysismentioning

confidence: 99%

Bayesian Fusion of Multi-Scale Detectors for Road Extraction from SAR Images

Fan

et al. 2017

IJGI

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This paper introduces an innovative road network extraction algorithm using synthetic aperture radar (SAR) imagery for improving the accuracy of road extraction. The state-of-the-art approaches, such as fraction extraction and road network optimization, failed to obtain continuous road segments in separate successions, since the optimization could not change the parts ignored by the fraction extraction. In this paper, the proposed algorithm integrates the fraction extraction and optimization procedure simultaneously to extract the road network: (1) the Bayesian framework is utilized to transfer the road network extraction to joint reasoning of the likelihood of fraction extraction and the priority of network optimization; (2) the multi-scale linear feature detector (MLFD) and the network optimization beamlet are introduced; (3) the conditional random field (CRF) is used to reason jointly. The result is the global optimum since the fraction extraction and network optimization are exploited at the same time. The proposed algorithm solves the problem that the fractions are bound to reduce in the process of network optimization and has demonstrated effectiveness in real SAR images applications.

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Section: Prior Constraints Under Beamlet Analysismentioning

confidence: 99%

Bayesian Fusion of Multi-Scale Detectors for Road Extraction from SAR Images

Fan

et al. 2017

IJGI

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“…Automatic road extraction algorithm can reduce both time and labor to construct and update the road spatial database in such applications. However, fully automated algorithms to recognize them for applications where accuracy is critical are very difficult [6,10,11]. In this paper, we utilize the high resolution remote sensing images, which can obviously enhance the object recognition accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Novel Road Extraction Algorithm for High Resolution Remote Sensing Images

Teng¹,

Shun-lin²,

Zhan³

2014

Appl. Math. Inf. Sci.

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Automatic road extraction from the high resolution remote sensing images is of great importance in intelligent transportation and image processing. Hence, in this paper, an effective road extraction algorithm for high resolution remote sensing images based on the circular projection transformation is proposed. The main idea of the proposed algorithm lies in that the road extraction results are obtained by selecting a suitable initial template, and then search the matched templates through moving the initial template in two directions. Firstly, the circular projection vector of the initial template is achieved by calculating the circular projection value at a specific radius. Secondly, the optimal radius of the circle in circular projection transformation and the length of the seeking step and the seeking angle are determined. Thirdly, for each seeking step the similarity between the target template and the initial template is computed, and the template with the highest similarity is chosen. Finally, roads can be detected by the correct direction by exchanging the first two detected points. To make performance evaluation, the IKONOS dataset is utilized and DMES and AUA algorithm are compared. The experimental results demonstrate that the proposed algorithm can automatic the roads from high resolution remote sensing images effectively and efficiently.

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“…Used fusion strategies are tested and compared. Jeon et al [4] present a genetic algorithm based road detection method. They use perceptual grouping factors to design the fitness function.…”

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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<title>Road detection in spaceborne SAR images using genetic algorithm</title>

Cited by 4 publications

References 7 publications

Bayesian Fusion of Multi-Scale Detectors for Road Extraction from SAR Images

Bayesian Fusion of Multi-Scale Detectors for Road Extraction from SAR Images

A Novel Road Extraction Algorithm for High Resolution Remote Sensing Images

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

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