Unveiling the Influence of Image Super-Resolution on Aerial Scene Classification

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Resumen

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.
Idioma originalInglés
Título de la publicación alojadaProgress In Pattern Recognition, Image Analysis, Computer Vision, And Applications, Ciarp 2023, Pt I
EditoresVerónica Vasconcelos, Inês Domingues, Simão Paredes
EditorialSpringer Nature
Páginas214-228
Número de páginas15
Volumen14469
ISBN (versión digital)978-3-031-49018-7
ISBN (versión impresa)978-3-031-49017-0
DOI
EstadoPublicada - 27 nov 2023

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen14469 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

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