Awais, Ch Muhammad, Reggiannini, Marco, Moroni, Davide and Karakus, Oktay  ORCID: https://orcid.org/0000-0001-8009-9319
2026.
A classification-aware super-resolution framework for ship targets in SAR imagery.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
19
, pp. 6614-6622.
10.1109/jstars.2026.3655550
|
Preview |
PDF
- Published Version
Available under License Creative Commons Attribution. Download (1MB) | Preview |
Abstract
High-resolution imagery plays a critical role in improving the performance of visual recognition tasks such as classification, detection, and segmentation. In many domains, including remote sensing and surveillance, low-resolution images can limit the accuracy of automated analysis. To address this, super-resolution (SR) techniques have been widely adopted to attempt to reconstruct high-resolution images from low-resolution inputs. Related traditional approaches focus solely on enhancing image quality based on pixel-level metrics, leaving the relationship between super-resolved image fidelity and downstream classification performance largely underexplored. This raises a key question: can integrating classification objectives directly into the super-resolution process further improve classification accuracy? In this paper, we try to respond to this question by investigating the relationship between super-resolution and classification through the deployment of a specialised algorithmic strategy. We propose a novel methodology that increases the resolution of synthetic aperture radar imagery by optimising loss functions that account for both image quality and classification performance. Our approach improves image quality, as measured by scientifically ascertained image quality indicators, while also enhancing classification accuracy.
| Item Type: | Article |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Publisher: | Institute of Electrical and Electronics Engineers |
| ISSN: | 1939-1404 |
| Date of First Compliant Deposit: | 26 January 2026 |
| Date of Acceptance: | 15 January 2026 |
| Last Modified: | 04 Mar 2026 15:29 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/184207 |
Actions (repository staff only)
 |
Edit Item |
Dimensions
Dimensions