  It is very useful and increasingly popular to extract accurate road
centerlines from very-high-resolution (VHR) re- mote sensing imagery for
various applications, such as road map generation and updating etc. There are
three shortcomings of current methods: (a) Due to the noise and occlusions
(owing to vehicles and trees), most road extraction methods bring in
heterogeneous classification results; (b) Morphological thinning algorithm is
widely used to extract road centerlines, while it pro- duces small spurs around
the centerlines; (c) Many methods are ineffective to extract centerlines around
the road intersections. To address the above three issues, we propose a novel
method to ex- tract smooth and complete road centerlines via three techniques:
the multiscale joint collaborative representation (MJCR) & graph cuts (GC),
tensor voting (TV) & non-maximum suppression (NMS) and fitting based connection
algorithm. Specifically, a MJCR-GC based road area segmentation method is
proposed by incorporating mutiscale features and spatial information. In this
way, a homogenous road segmentation result is achieved. Then, to obtain a
smooth and correct road centerline network, a TV-NMS based centerline
extraction method is introduced. This method not only extracts smooth road
centerlines, but also connects the discontinuous road centerlines. Finally, to
overcome the ineffectiveness of current methods in the road intersection, a
fitting based road centerline connection algorithm is proposed. As a result, we
can get a complete road centerline network. Extensive experiments on two
datasets demonstrate that our method achieves higher quantitative results, as
well as more satisfactory visual performances by comparing with state-of-the-
art methods.
