High-Resolution Reconstructions of Aerial Images Based On Deep Learning

Authors

  • Armando Levid Rodríguez-Santiago Universidad Tecnológica de la Mixteca
  • José Aníbal Arias-Aguilar Universidad Tecnológica de la Mixteca
  • Hiroshi Takemura Tokyo University of Science, Faculty of Science and Technology
  • Alberto Elías Petrilli-Barcelo Tokyo University of Science, Faculty of Science and Technology

DOI:

https://doi.org/10.13053/cys-25-4-4047

Keywords:

Deep learning, CNN, 2D reconstruction, aerial images, orthophotography, photogrammetry

Abstract

We present a methodology for high-resolution orthomosaic reconstruction using aerial images. Our proposal consists a neural network with two main stages, one to obtain the correspondences necessary to perform a LR-orthomosaic and another one that uses these results to generate an HR- orthomosaic, and a feedback connection. The CNN are based onwell known models and are trained to perform image stitching and obtain a high-resolution orthomosaic. The results obtained in this work show that our methodology provides similar results to those obtained by an expertin orthophotography, but in high-resolution.

Author Biographies

Hiroshi Takemura, Tokyo University of Science, Faculty of Science and Technology

Department of Mechanical Engineering

Alberto Elías Petrilli-Barcelo, Tokyo University of Science, Faculty of Science and Technology

Department of Mechanical Engineering

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Published

2021-11-30

Issue

Vol. 25 No. 4 (2021): 25(4) 2021: Thematic Issue on Artificial Intelligence for Industry 4.0 (Guest Editors: O. Vergara et al.)

Section

Articles of the Thematic Issue

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