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.

翻译：高分辨率图像在提升分类、检测与分割等视觉识别任务性能方面具有关键作用。在遥感与监视等诸多领域，低分辨率图像会限制自动化分析的准确性。为此，超分辨率技术已被广泛采用，试图从低分辨率输入重建高分辨率图像。相关的传统方法仅关注基于像素级指标的图像质量增强，而超分辨率图像保真度与下游分类性能之间的关系在很大程度上尚未得到充分探索。这引出了一个核心问题：将分类目标直接整合到超分辨率过程中能否进一步提升分类精度？本文通过部署专门的算法策略研究超分辨率与分类之间的关系，试图回应这一问题。我们提出了一种新颖的方法，通过优化同时考虑图像质量与分类性能的损失函数，来提高合成孔径雷达图像的分辨率。该方法在通过科学验证的图像质量指标衡量下改善了图像质量，同时提升了分类准确率。