Road Extraction from High-Resolution SAR Images via Automatic Local Detecting and Human-Guided Global Tracking

@article{Cheng2012RoadEF,
  title={Road Extraction from High-Resolution SAR Images via Automatic Local Detecting and Human-Guided Global Tracking},
  author={Jianghua Cheng and Wenxia Ding and Xishu Ku and Jixiang Sun},
  journal={International Journal of Antennas and Propagation},
  year={2012},
  volume={2012},
  pages={1-10}
}
Because of existence of various kinds of disturbances, layover effects, and shadowing, it is difficult to extract road from high-resolution SAR images. A new road center-point searching method is proposed by two alternant steps: local detection and global tracking. In local detection step, double window model is set, which consists of the outer fixed square window and the inner rotary rectangular one. The outer window is used to obtain the local road direction by using orientation histogram… 
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References

SHOWING 1-10 OF 24 REFERENCES
Semi-automatic road centerline extraction in high-resolution SAR images based on circular template matching
TLDR
A new semi-automatic road centerline extraction method based on circular template matching that is effective in high resolution SAR images and linked by quadratic curve fitting.
Semi-automatic extraction of road networks by least squares interlaced template matching in urban areas
TLDR
Results show that the proposed semi-automatic method is capable of robustly extracting roads from VHR imagery because the special configuration of the reference template can decrease side effects such as the occlusion of vehicles and the shadow of trees as much as possible.
Tracking Road Centerlines from High Resolution Remote Sensing Images by Least Squares Correlation Matching
This paper describes a semi-automatic algorithm for tracking road centerlines from satellite images at 1 m resolution. We assume that road centerlines are visible in the image and that among points
A template-matching based approach for extraction of roads from very high-resolution remotely sensed imagery
TLDR
This article proposes an approach for semi-automated extraction of road networks by tracking apparent lane markings and/or median strips on VHR imagery and can successfully track over 94% of the highways and 81%" of the arterial roads from the VHR images, and save the time of 26% when comparing to traditional methods.
Extracting road centrelines from high-resolution satellite images using active window line segment matching and improved SSDA
TLDR
The proposed approach is capable of rapidly and accurately extracting main road centrelines and has good robustness against noise and allows some kinds of user interventions in case automatic tracking fails.
Road Extraction From Satellite Images Using Particle Filtering and Extended Kalman Filtering
TLDR
A method for dynamic clustering of the road profiles in order to maintain tracking when the road profile undergoes some variations due to changes in the road width and intensity is constructed.
Combining Multiple Algorithms for Road Network Tracking from Multiple Source Remotely Sensed Imagery: a Practical System and Performance Evaluation
TLDR
A novel classification of roads is proposed, based on both the roads' geometrical, radiometric properties and the characteristics of the sensors, and it is proven that a combination of multiple algorithms is more reliable, more efficient and more robust for extracting road networks from multiple-source remotely sensed imagery than the individual algorithms.
Feature fusion to improve road network extraction in high-resolution SAR images
TLDR
A road extraction method which is based on the fusion of classification and line detection at the feature level, which helps to improve both the level of likelihood and the number of the extracted roads.
Road tracking in aerial images based on human–computer interaction and Bayesian filtering
Junction-aware extraction and regularization of urban road networks in high-resolution SAR images
A general processing framework for urban road network extraction in high-resolution synthetic aperture radar images is proposed. It is based on novel multiscale detection of street candidates,
...
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