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Convolutional Neural Network with Fourier Transform for Road Classification from Satellite Images

In: New Trends in Computational Vision and Bio-inspired Computing

Author

Listed:
  • Jose Hormese

    (NIT Durgapur, Department of Computer Science and Engineering)

  • Chandran Saravanan

    (NIT Durgapur, Department of Computer Science and Engineering)

Abstract

To provide the good range of image, the satellite image is used for road classification which consist very high resolution. A major challenge is that a road network has lot of topological irregularity. There are numerous applications to road classifications namely in the areas of designing emergency rescue systems, updating geographic information systems and roads navigation. This manuscript is deals with the extraction of the road network from the satellite perspectives that have high resolution. This proposed work deals with approximating whether the image pixel is the part of a road or which is not consuming a Convolutional Neural Network (CNN). This proposed work recommends a new innovative tactic for creating data sets for this intricate problem and has accomplished with a viable resolution for the given tricky problem. An attempt with passing Fourier Transform of the input image has contributed to better performance of the CNN in terms of small block size and thereby fast learning of the network.

Suggested Citation

  • Jose Hormese & Chandran Saravanan, 2020. "Convolutional Neural Network with Fourier Transform for Road Classification from Satellite Images," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 1257-1264, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_127
    DOI: 10.1007/978-3-030-41862-5_127
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