TY  - CONF
AU  - Mohamed Ramzy Ibrahim
AU  - Robert Benavente
AU  - Daniel Ponsa
AU  - Felipe Lumbreras
A2  - CIARP
PY  - 2023//
TI  - Unveiling the Influence of Image Super-Resolution on Aerial Scene Classification
T2  - LNCS
BT  - Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
SP  - 214–228
VL  - 14469
N2  - 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.
UR  - https://doi.org/10.1007/978-3-031-49018-7_16
N1  - MSIAU
ID  - Mohamed Ramzy Ibrahim2023
ER  -