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

2025-01-12

AI Summary

Low-resolution remote sensing images severely limit the accuracy of multi-label classification tasks. Method: This paper proposes a plug-and-play super-resolution (SR) enhancement framework, systematically evaluating the downstream classification gains of four state-of-the-art SR models—SRResNet, HAT, SeeSR, and RealESRGAN. The SR module is integrated as a lightweight preprocessing layer and jointly optimized with CNN classifiers including ResNet-50/101/152 and Inception-v4. Results: SR preprocessing consistently improves key metrics such as mean average precision (mAP) and F1-score by recovering discriminative spatial details. This work presents the first comprehensive empirical validation of SR model generalizability and stability in remote sensing multi-label classification. It delivers a reproducible, robust performance-boosting pathway, establishing a novel paradigm for analyzing low-quality remote sensing imagery.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Remote Sensing / Geospatial AI
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Low-resolution satellite images
• Object recognition
• Challenges

Innovation

• Image Super-Resolution
• Satellite Imagery Enhancement
• Multi-Object Recognition Accuracy