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

Jose Hormese and Chandran Saravanan
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Jose Hormese: NIT Durgapur, Department of Computer Science and Engineering
Chandran Saravanan: NIT Durgapur, Department of Computer Science and Engineering

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 1257-1264 from Springer

Abstract: 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.

Keywords: GIS (geographic information systems); Satellite image; Road classification; CNN (convolutional neural network); Tensor flow (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41862-5_127

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DOI: 10.1007/978-3-030-41862-5_127

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