Road vectorisation from high‐resolution imagery based on dynamic clustering using particle swarm optimisation

Ameri, Fateme; Zoej, Mohammad Javad Valadan

doi:10.1111/phor.12123

Cited by 9 publications

(14 citation statements)

References 22 publications

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“…Figure 10 illustrates the achieved improvement with respect to all three datasets. The dataset used in this experiment is a part of a scene that is recently used in the research works of Ameri et al [25], Miao et al [20] and Nikfar et al [18]. In Table 3 All methods fulfilled the lowest limits for completeness and correctness recommended in [8].…”

Section: Discussion On Tensor Votingmentioning

confidence: 99%

“…In road extraction from VHR images significant progress has been achieved by the remote sensing and photogrammetry community, e.g., [17,18,20,25,30], and by computer science researchers, e.g., [1,10,12,14,31]. Nevertheless, the ultimate goal of designing and implementing a reliable automatic road extraction system is still far-off [6,12,20,21,29].…”

Section: Proposed Approach and Methodsmentioning

confidence: 99%

“…The dataset used in this experiment is a part of a scene that is recently used in the research works of Ameri et al [25], Miao et al [20] and Nikfar et al [18]. In Table 3 The dataset used in this experiment is a part of a scene that is recently used in the research works of Ameri et al [25], Miao et al [20] and Nikfar et al [18]. In Table 3 All methods fulfilled the lowest limits for completeness and correctness recommended in [8].…”

Section: Third Experimentsmentioning

confidence: 99%

“…SOLI, which estimates the length of the object by the length of its main skeleton, can efficiently quantify the linearity of these curved and branched objects. To consider the second characteristic, i.e., a small region, is not likely to be a road segment, areabased filtering [14,25] is employed. Moreover, we add contextual information to avoid the elimination of small road segments which are connected to at least two road segments (detected by SOLI).…”

Section: Object-based Feature Integrationmentioning

confidence: 99%

“…Ameri and Valadan [25] motivated by research of Doucette et al [26], introduced a road vectorization method based on dynamic clustering. This approach utilizes a swarm-based optimization technique that relies on a modified cost function.…”

Section: Introductionmentioning

confidence: 99%

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Road Network Extraction from VHR Satellite Images Using Context Aware Object Feature Integration and Tensor Voting

et al. 2016

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Road networks are very important features in geospatial databases. Even though high-resolution optical satellite images have already been acquired for more than a decade, tools for automated extraction of road networks from these images are still rare. One consequence of this is the need for manual interaction which, in turn, is time and cost intensive. In this paper, a multi-stage approach is proposed which integrates structural, spectral, textural, as well as contextual information of objects to extract road networks from very high resolution satellite images. Highlights of the approach are a novel linearity index employed for the discrimination of elongated road segments from other objects and customized tensor voting which is utilized to fill missing parts of the network. Experiments are carried out with different datasets. Comparison of the achieved results with the results of seven state-of-the-art methods demonstrated the efficiency of the proposed approach.

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Section: Discussion On Tensor Votingmentioning

confidence: 99%