Abstract
Roads are an important recognition target in synthetic aperture radar (SAR) image interpretation. Although a considerable number of high-quality SAR images are now available, the method of road extraction is lagging. To extract the road network with low missed and false rates, this paper proposed a road network extraction approach which includes line detection, road segmentation, road network extraction and optimization. First, the linear feature response and direction map are obtained from the SAR intensity image using the multiplicative Duda operation. Then, the backscattering coefficient and coefficient of variation are combined using a support vector machine to eliminate the linear structures of non-roads, and the binary image of road candidates is subsequently achieved by morphological profiles of path openings. Next, with the obtained direction map, a novel thinning method based on binary image decomposition and curve fitting is presented to obtain line segments of the road network. Finally, a series of measures which involve overlap, continuity, and junction optimization are proposed to construct the road network. In the experiments, the proposed method was applied to Radarsat-2 and TerraSAR-X high-resolution images. The experimental results showed that the proposed method had an excellent performance in terms of both completeness and correctness.
Highlights
Roads are typical man-made objects that play an essential role in modern transportation systems
We develop a road network extraction approach based on the aforementioned two steps
It is certain that all the road candidates on an image layer Bk have similar directional characteristics that belongs to the set Gk
Summary
Roads are typical man-made objects that play an essential role in modern transportation systems. Considering these road features, many different kinds of methods have been presented to detect road from SAR images in the past decades Among those approaches, a significant number are comprise two main steps: one for local linear feature detection aiming to achieve road candidates, and one for global optimization processing, which generates regular road lines and connects the gaps to form a road network. For the method in this paper, the binary image decomposition according to direction features and thinning based on polynomial fitting are proposed to extract line segments. These proposed approaches are motivated by the idea that the road network is composed of cross-linked curves with determined equations. For a labeled image T , we define a binary conversion to extract the pixels that belong to a label set G as follows: For a labeled image T, we define a binary conversion to extract the pixels that belong to a label set
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