TY - CHAP
T1 - Unveiling the Influence of Image Super-Resolution on Aerial Scene Classification
AU - Ibrahim, Mohamed Ramzy
AU - Benavente, Robert
AU - Ponsa, Daniel
AU - Lumbreras, Felipe
N1 - Publisher Copyright:
© 2024, Springer Nature Switzerland AG.
PY - 2023/11/27
Y1 - 2023/11/27
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.
AB - 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.
KW - Aerial images
KW - Deep learning
KW - Remote sensing
KW - Scene classification
KW - Super-resolution
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=uab_pure&SrcAuth=WosAPI&KeyUT=WOS:001148044200016&DestLinkType=FullRecord&DestApp=WOS_CPL
UR - http://www.scopus.com/inward/record.url?scp=85178570751&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/e3916b32-4ada-3abc-a79d-2c4abbb0ba8a/
U2 - 10.1007/978-3-031-49018-7_16
DO - 10.1007/978-3-031-49018-7_16
M3 - Chapter
SN - 978-3-031-49017-0
VL - 14469
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 214
EP - 228
BT - Progress In Pattern Recognition, Image Analysis, Computer Vision, And Applications, Ciarp 2023, Pt I
A2 - Vasconcelos, Verónica
A2 - Domingues, Inês
A2 - Paredes, Simão
PB - Springer Nature
ER -