超分辨率提升遥感图像细节，改善多标签场景分类效果
Multi-Label Scene Classification in Remote Sensing Benefits from Image Super-Resolution
- 用四种超分辨率模型预处理遥感图像，增强细节
- 在多个CNN架构上验证，分类性能显著提升
- 适合希望改进遥感多标签分类的工程师和研究者
卫星影像是众多遥感应用的核心，但受限于空间分辨率，常影响系统精度，尤其在多标签场景分类任务中，需更高细节与特征区分能力。本文探索将图像超分辨率（SR）作为预处理步骤，以提升卫星图像质量并改善下游分类表现。我们测试了四种SR模型：SRResNet、HAT、SeeSR和RealESRGAN，评估其在ResNet-50、ResNet-101、ResNet-152及Inception-v4等多种卷积神经网络上的影响。结果表明，采用超分辨率能显著提升多种指标下的分类性能，证明其在保留多标签任务关键空间细节方面的有效性。本研究为遥感中多标签预测的SR技术选择提供实用参考，并提出一个易于集成的优化框架。
原文摘要 · Abstract (English)
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.
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