Snakes models can effectively fit curve features and are thus widely used to extract roads from remote sensing images. However

when used to extract roads from synthetic aperture radar (SAR) images

traditional Snakes models that utilize the negative gradient of images as external energy cannot obtain the desired results because of serious speckle noise. To address this issue

we employed a tensor voting method in improving Snakes models because such method can extract salient structures from images influenced by noises. Road class was first segmented from SAR images using the FCM clustering method. Then

the saliency value of the curve features was obtained by employing the tensor voting method on the extracted road class. Finally

the negative normalized saliency value of the curve features was used as the external energy of the snakes model to extract roads. To minimize the energy of the snakes model

a strategy for minimizing energy while interpolating nodes was proposed. Road extraction experiments were performed on different scenes of spaceborne and airborne SAR images. Compared with a similar method based on the snakes model

the proposed modelachieved better fitting results with less control points. Moreover

the proposed method showed better detection completeness

correctness

and quality than the MRF-based method. The proposed method also demonstrated a shorter detection time

which is a practical feature for wide-range road network extraction. The proposed method quantified the geometric characteristics of roads through tensor voting. An optimized fitting strategy was used to minimize the energy consumption of the snakes model for road extraction. The experiments on spaceborne and airborne SAR images proved that main roads in rural and urban scenes can be effectively extracted using the proposed method.