Abstract
This paper introduces an innovative road network extraction algorithm using synthetic aperture radar (SAR) imagery for improving the accuracy of road extraction. The state-of-the-art approaches, such as fraction extraction and road network optimization, failed to obtain continuous road segments in separate successions, since the optimization could not change the parts ignored by the fraction extraction. In this paper, the proposed algorithm integrates the fraction extraction and optimization procedure simultaneously to extract the road network: (1) the Bayesian framework is utilized to transfer the road network extraction to joint reasoning of the likelihood of fraction extraction and the priority of network optimization; (2) the multi-scale linear feature detector (MLFD) and the network optimization beamlet are introduced; (3) the conditional random field (CRF) is used to reason jointly. The result is the global optimum since the fraction extraction and network optimization are exploited at the same time. The proposed algorithm solves the problem that the fractions are bound to reduce in the process of network optimization and has demonstrated effectiveness in real SAR images applications.
Highlights
Tupin et al [8] proposed a two-step technique with Detector 1 (D1) and D2 operators to extract the main candidates; a Markov random field (MRF) based on the graph composed of local segments was built to optimize road construction
(1) The main contribution of this paper is the introduction of two road detection methods for multi-scale analysis and fusing them using a Bayesian framework to fully utilize the strengths of multi-scale analysis
The association optimization of conditional random field (CRF) leads to a better road network compared with the separate detection methods
Summary
Synthetic aperture radar (SAR) imagery has considerable real-world applications, such as mapping, remote sensing, urban planning, agriculture and disaster prevention [1]. Among these applications, road extraction is of substantial research interest because linear targets (including roads, bridges, ridge lines and coast lines) appear with considerable darkness in SAR images due to the odd scattering [2]. The two general steps of road extraction from SAR images are local road candidate segment detection and global road network optimization. Tupin et al [8] proposed a two-step technique with Detector 1 (D1) and D2 operators to extract the main candidates; a Markov random field (MRF) based on the graph composed of local segments was built to optimize road construction. The optimization process was implemented by minimizing an energy function using simulated annealing
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