Multi-Label Scene Classification in Remote Sensing Benefits from Image Super-Resolution
This work addresses the need for higher detail in remote sensing systems for applications like multi-label scene classification, though it is incremental as it applies existing super-resolution methods to this domain.
The study tackled the problem of limited spatial resolution hindering multi-label scene classification in satellite imagery by exploring image super-resolution as a pre-processing step, finding that it significantly improves classification performance across various metrics.
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.