# A Classification-Aware Super-Resolution Framework for Ship Targets in SAR Imagery

arXiv: https://arxiv.org/abs/2508.06407 · HTML: https://mlsanity.com/paper/2508.06407/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 54 references that are in the map.
- First posted on arXiv 8 Aug 2025.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [A Deep Journey into Super-resolution: A survey](https://mlsanity.com/paper/1904.07523/) (2019)
- [Revisiting L1 Loss in Super-Resolution: A Probabilistic View and Beyond](https://mlsanity.com/paper/2201.10084/) (2022)
- [On Solving SAR Imaging Inverse Problems Using Non-Convex Regularization with a Cauchy-based Penalty](https://mlsanity.com/paper/2005.00657/) (2020)
- [SAR Ship Target Recognition Via Multi-Scale Feature Attention and Adaptive-Weighed Classifier](https://mlsanity.com/paper/2308.10247/) (2023)

## Built on by (most cited first, 1 of 1)

- [ProSR: Semantic-Prototype-Guided Discrete Modeling for Physically Consistent SAR Super-Resolution](https://mlsanity.com/paper/2609.02377/) (2026)

## Abstract (arXiv)

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.