Unveiling the influence of image super-resolution on aerial scene classification
Ibrahim, Mohamed Ramzy (Centre de Visió per Computador)
Benavente i Vidal, Robert (Universitat Autònoma de Barcelona. Departament de Ciències de la Computació)
Ponsa Mussarra, Daniel (Universitat Autònoma de Barcelona. Departament de Ciències de la Computació)
Lumbreras Ruiz, Felipe (Universitat Autònoma de Barcelona. Departament de Ciències de la Computació)

Publicació:	Cham, Switzerland: Springer, 2024
Descripció:	15 pàg.
Resum:	Deep learning has made significant advances in recent years, and as a result, it is now in a stage where it can achieve outstanding results in tasks requiring visual understanding of scenes. However, its performance tends to decline when dealing with low-quality images. The advent of super-resolution (SR) techniques has started to have an impact on the field of remote sensing by enabling the restoration of fine details and enhancing image quality, which could help to increase performance in other vision tasks. However, in previous works, contradictory results for scene visual understanding were achieved when SR techniques were applied. In this paper, we present an experimental study on the impact of SR on enhancing aerial scene classification. Through the analysis of different state-of-the-art SR algorithms, including traditional methods and deep learning-based approaches, we unveil the transformative potential of SR in overcoming the limitations of low-resolution (LR) aerial imagery. By enhancing spatial resolution, more fine details are captured, opening the door for an improvement in scene understanding. We also discuss the effect of different image scales on the quality of SR and its effect on aerial scene classification. Our experimental work demonstrates the significant impact of SR on enhancing aerial scene classification compared to LR images, opening new avenues for improved remote sensing applications.
Ajuts:	Agencia Estatal de Investigación PID2021-128945NB-I00 Agència de Gestió d'Ajuts Universitaris i de Recerca 2021/SGR-01499
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Llengua:	Anglès
Títol addicional:	Lecture notes in computer science 14469
Document:	Capítol de llibre ; recerca ; Versió acceptada per publicar
Matèria:	Aerial images ; Deep learning ; Remote sensing ; Scene classification ; Super-resolution
Publicat a:	26th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2023, 2024, p. 214-228, ISBN 978-3-031-49018-7

DOI: 10.1007/978-3-031-49018-7_16

Postprint 16 p, 1.4 MB

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