Road extraction from satellite images by self-supervised classification and perceptual grouping

Date

2013-01-01

Author

Sahin, E.
Ulusoy, İlkay

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A fully automatized method which can extract road networks by using the spectral and structural features of the roads is proposed. First, Anti-parallel Centerline Extraction (ACE) is used to obtain road seed points. Then, the road seeds are improved with perceptual grouping method and the road class is determined with Maximum Likelihood Estimation (MLE) by modeling the seed points with Gaussian Mixture. The morphological operations (opening, closing and thinning) are performed for improving classification results and determining the road topology roughly. Finally, perceptual grouping is performed for removing non-road line segments and filling the gaps on the topology. The proposed algorithm is tested on 1 meter resolution IKONOS images and results better than previous algorithms are obtained

Subject Keywords

Anti-parallel Centerline, Gaussian Mixture, Perceptual grouping, Classification

URI

https://hdl.handle.net/11511/52181

DOI

https://doi.org/10.1117/12.2028672

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

E. Sahin and İ. Ulusoy, “Road extraction from satellite images by self-supervised classification and perceptual grouping,” 2013, vol. 8892, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/52181.