Multi-Label Scene Classification in Remote Sensing Benefits from Image Super-Resolution

TL;DR

This paper demonstrates that applying image super-resolution techniques as a pre-processing step significantly enhances the accuracy of multi-label scene classification in satellite imagery, benefiting remote sensing applications.

Contribution

It evaluates multiple super-resolution models and CNN architectures, providing insights into their combined impact on classification performance in remote sensing.

Findings

Super-resolution improves classification accuracy across models.

SR techniques preserve spatial details critical for multi-label tasks.

The framework is easy to integrate into existing RS systems.

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.