Road detection in spaceborne SAR images based on ridge extraction

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

doi:10.1109/icip.1999.822993

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…Many researchers have proposed various methods for road recognition [1][2][3][4]. Tupin [1] uses Markov Random Field to link line segments which are detected locally to form fully roads.…”

Section: Introductionmentioning

confidence: 99%

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Road recognition in high resolution SAR image based on genetic algorithm

Zhang²,

Zhang

2008

2008 7th World Congress on Intelligent Control and Automation

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Road recognition from SAR image is a very important problem. In this paper, a new method is proposed which can detect multiple roads in an image. Firstly binary edge image is gained by Canny operator and edge refining. Secondly, edge curves are encoded by reversible chained list, and fitness function is constructed based on SAR image road models, and optimal road boundary curves are linked by improved Genetic Algorithm. Thirdly, complete road boundaries are detected by curve fitting. At last, mated long road edge curves are found out and road surface are stuffed. The experiment shows that this algorithm can realize roads recognition and eliminate influence of other non-road.

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“…Many researchers have proposed various methods for road recognition [1][2][3][4]. Tupin [1] uses Markov Random Field to link line segments which are detected locally to form fully roads.…”

Section: Introductionmentioning

confidence: 99%

“…Tupin [1] uses Markov Random Field to link line segments which are detected locally to form fully roads. Paper [4] applies Snakes Model to detecting roads. The algorithm for grouping curve lines based on Genetic Algorithm (GA) to detecting roads by Jeon [2,3] is one of the most excellent algorithms.…”

Section: Introductionmentioning

confidence: 99%

Road recognition in high resolution SAR image based on genetic algorithm

Zhang²,

Zhang

2008

2008 7th World Congress on Intelligent Control and Automation

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Road detection in spaceborne SAR images using a genetic algorithm

Jeon¹,

Jang

Hong

2002

IEEE Trans. Geosci. Remote Sensing

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Abstract-This paper presents a technique for the detection of roads in a spaceborne synthetic aperture radar (SAR) image using a genetic algorithm (GA). Roads in a spaceborne SAR image can be modeled as curvilinear structures that possess width. Curve segments, which represent the candidate positions for roads, are extracted from the image using a curvilinear structure detector, and the roads are accurately detected by grouping those curve segments. For this purpose, we designed a grouping method based on a GA, which is a global optimization method. We combined perceptual grouping factors with it and tried to reduce its overall computational cost by introducing a concept of region growing. In this process, a selected initial seed is grown into a finally grouped segment by the iterated GA process, which considers segments only in a search region. To detect roads more accurately, postprocessing, including noisy curve segment removal, is performed after grouping. We applied our method to ERS-1 SAR and SIR-C/X-SAR images that have a resolution of about 30 m. The experimental results show that our method can accurately detect road networks as well as single-track roads and is much faster than a globally applied GA approach.Index Terms-Genetic algorithm (GA), perceptual grouping, road detection, synthetic aperture radar (SAR).

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A Method of Linear Feature Extraction from High Resolution Aerial Images

Liu

Peng

2009

2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery

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Road detection in spaceborne SAR images based on ridge extraction

Cited by 6 publications

References 10 publications

Road recognition in high resolution SAR image based on genetic algorithm

Road recognition in high resolution SAR image based on genetic algorithm

Road detection in spaceborne SAR images using a genetic algorithm

A Method of Linear Feature Extraction from High Resolution Aerial Images

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