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Finally, Markov random field is defined upon this graph to extract road network in satellite images.
A hybrid method of using artificial neural network, contour tracing algorithm, and morphological trivial opening is used to extract road from remotely sensed image and is presented by Sowmya et al. [30].
This method consists of four stages: segmentation of remotely sensed image; then extraction of road region based on shape and spectral feature; after that, application of multivariate adaptive regression splines to extract road centerline; finally, the use of connection algorithm to get complete road network.
In the data preparation phase, topographic maps at the scale of 1 25,000 were used to extract road and drainage layers.
Step 4: Extract road network geospatial data Select the "ArcToolbox" icon, navigate to "Analysis Tools", then to "Overlay" then to "Intersect".
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Narrow linear disturbance features have been detected in forests used both very high resolution (VHR) QuickBird and medium resolution SPOT imagery [49] and object-based image analysis has been used to extract roads from a variety of backgrounds [50].
The correctness represents the percentage of correctly extracted road data, i.e., the ratio between matched parts of extracted road network with total length of extracted road network.
TP - true positive, an element present in ground truth and extracted road network.
The final extracted road map in all noisy level is almost equal to noiseless result.
FN - false negative, an element present in ground truth but not in extracted road network.
FP - false positive, an element present in extracted road network but not in ground truth.
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