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| Format: | Artículo científico |
| Langue: | en |
| Publié: |
Universidade Federal do Paraná
2022
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| Accès en ligne: | https://www.redalyc.org/articulo.oa?id=393973723002 https://www.redalyc.org/journal/3939/393973723002/ https://www.redalyc.org/journal/3939/393973723002/html/ https://www.redalyc.org/journal/3939/393973723002/393973723002.epub https://www.redalyc.org/journal/3939/393973723002/movil |
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Table des matières:
- Enhancing SRTM digital elevation models with deep-learning-based super-resolution image generation Leonardo Assumpção Moreira Livia Moreira Poelking Hideo Araki Ciencias de la Tierra Deep Learning Neural Networks Machine Learning Image Super Resolution Digital Elevation Model Digital elevation models are responsible for providing altimetric information on a surface to be mapped. While global models of low and medium spatial resolution are available open source by several space agencies, the high-resolution ones, which are utilized in scales 1:25,000 and larger, are scarce and expensive. Here we address this limitation by the utilization of deep learning algorithms coupled with SISR techniques in digital elevation models to obtain better spatial quality versions from lower resolution inputs. The development of a GAN-based methodology enables the improvement of the initial spatial resolution of low-resolution images. A dataset with different pairs of digital elevation models was created with the objective of allowing the study to be carried out, promoting the emergence of new research groups in the area as well as enabling the comparison between the results obtained. It has been found that by increasing the number of iterations the performance of the generated model was improved and the quality of the generated image increased. Furthermore, the visual analysis of the generated image against the high- and low-resolution ones showed a great similarity between the first two. 2022 artículo científico 1413-4853 https://www.redalyc.org/articulo.oa?id=393973723002 https://www.redalyc.org/journal/3939/393973723002/ https://www.redalyc.org/journal/3939/393973723002/html/ https://www.redalyc.org/journal/3939/393973723002/393973723002.epub https://www.redalyc.org/journal/3939/393973723002/movil 10.1590/s1982-21702022000400023 en http://www.redalyc.org/revista.oa?id=3939 Boletim de Ciências Geodésicas application/pdf Universidade Federal do Paraná Boletim de Ciências Geodésicas (Brasil) Num.4 Vol.28