Automatic generation of aerial orthoimages using sentinel-2 satellite imagery with a context-based deep learning approach

Suhong Yoo, Jisang Lee, Junsu Bae, Hyoseon Jang, Hong Gyoo Sohn

Research output: Contribution to journalArticlepeer-review

Abstract

Aerial images are an outstanding option for observing terrain with their high-resolution (HR) capability. The high operational cost of aerial images makes it difficult to acquire periodic observation of the region of interest. Satellite imagery is an alternative for the problem, but low-resolution is an obstacle. In this study, we proposed a context-based approach to simulate the 10 m resolution of Sentinel-2 imagery to produce 2.5 and 5.0 m prediction images using the aerial or-thoimage acquired over the same period. The proposed model was compared with an enhanced deep super-resolution network (EDSR), which has excellent performance among the existing super-resolution (SR) deep learning algorithms, using the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and root-mean-squared error (RMSE). Our context-based ResU-Net outperformed the EDSR in all three metrics. The inclusion of the 60 m resolution of Sentinel-2 imagery performs better through fine-tuning. When 60 m images were included, RMSE decreased, and PSNR and SSIM increased. The result also validated that the denser the neural network, the higher the quality. Moreover, the accuracy is much higher when both denser feature dimensions and the 60 m images were used.

Original languageEnglish
Article number1089
Pages (from-to)1-25
Number of pages25
JournalApplied Sciences (Switzerland)
Volume11
Issue number3
DOIs
Publication statusPublished - 2021 Feb 1

Bibliographical note

Funding Information:
This research was supported by a grant (no.20009742) from the Disaster-Safety Industry Promotion Program funded by the Ministry of Interior and Safety (MOIS, Korea).

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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