Abstract
Automatic network road extraction from high resolution remotely sensed images has been under study by computer scientists for over 30 years. In fact, Conventional methods to create and update road information rely heavily on manual work and therefore are very expensive and time consuming. This paper presents an efficient and computationally fast method to extract road from very high resolution images automatically. We propose in this paper a new approach for following roads path based on a quaternionic wavelet transform insuring a good local space-frequency analysis with very important directional selectivity. In fact, the rich phase information given by this hypercomplex transform overcomes the lack of shift invariance property shown by the real discrete wavelet transform and the poor directional selectivity of both real and complex wavelet transform.
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Naouai, M., Hamouda, A., Akkari, A., Weber, C. (2011). New Approach for Road Extraction from High Resolution Remotely Sensed Images Using the Quaternionic Wavelet. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds) Pattern Recognition and Image Analysis. IbPRIA 2011. Lecture Notes in Computer Science, vol 6669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21257-4_56
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DOI: https://doi.org/10.1007/978-3-642-21257-4_56
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