Abstract
Satellite imagery is a cornerstone for numerous Remote Sensing (RS) applications; however, limited spatial resolution frequently hinders the precision of such systems, especially in multi-label scene classification tasks as it requires a higher level of detail and feature differentiation. In this study, we explore the efficacy of image Super-Resolution (SR) as a pre-processing step to enhance the quality of satellite images and thus improve downstream classification performance. We investigate four SR models - SRResNet, HAT, SeeSR, and RealESRGAN - and evaluate their impact on multi-label scene classification across various CNN architectures, including ResNet-50, ResNet-101, ResNet-152, and Inception-v4. Our results show that applying SR significantly improves downstream classification performance across various metrics, demonstrating its ability to preserve spatial details critical for multi-label tasks. Overall, this work offers valuable insights into the selection of SR techniques for multi-label prediction in remote sensing and presents an easy-to-integrate framework to improve existing RS systems.
Abstract (translated)
卫星图像在众多遥感（RS）应用中扮演着基石角色；然而，有限的空间分辨率常常限制了这些系统的精度，特别是在多标签场景分类任务中，因为这类任务需要更高的细节层次和特征区分度。在这项研究中，我们探索了使用图像超分辨率（SR）作为预处理步骤来提升卫星图像质量，并从而提高下游分类性能的有效性。我们评估了四种SR模型——SRResNet、HAT、SeeSR 和 RealESRGAN，在包括 ResNet-50、ResNet-101、ResNet-152 以及 Inception-v4 等不同卷积神经网络（CNN）架构上的多标签场景分类任务中的影响。我们的研究结果表明，应用超分辨率技术显著提升了各种性能指标下的下游分类效果，展示了其在保持对多标签任务至关重要的空间细节方面的能力。总的来说，这项工作为选择适用于遥感中多标签预测的超分辨率技术提供了有价值的见解，并提供了一个易于集成的框架来提升现有RS系统。
URL
https://arxiv.org/abs/2501.06720
