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In this paper, a road network grouping algorithm for Synthetic Aperture Radar (SAR) images is proposed by exploiting multiscal
e geometric analysis of detector responses. Before running the algorithm, a response map made up of responses, which is binarized, skeletonized, and vectorized to generate road candidates, is obtained by applying a local detector to a SAR image first.
Then the proposed method identifies real road segments among
the candidates and fills gaps between them. It works in three
steps. 1) Guidance segments are extracted at different resolutions
from the response map using multiscale techniques and merged to
get a more appropriate approximation. 2) Segments are labeled
“road” or “noise” using relaxation labeling techniques, among
which “road” ones are grouped as they may lie on different roads.
3) Connection points between can didates are acquired by mapping
candidates to grouped “road” guidance segments. Those connection points are linked with straight lines or curvilinear segments after a segmentation process.
The experiments on TerraSAR images show the effectiveness of this new method.
CitationHe, C., Liao, Z., Yang, F., Deng, X., Liao, M. Road extraction from SAR imagery based on multiscale geometric analysis of detector responses. "IEEE journal of selected topics in applied earth observations and remote sensing", 2012, vol. 5, núm. 5, p. 1373-1382.
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