High-Resolution Reconstructions of Aerial Images Based On Deep Learning

Armando Levid Rodríguez-Santiago, José Aníbal Arias-Aguilar, Hiroshi Takemura, Alberto Elías Petrilli-Barcelo


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.


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

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