Quanv4EO: Empowering Earth Observation by means of Quanvolutional Neural Networks

TL;DR

This paper introduces Quanv4EO, a quantum-enhanced neural network framework that improves remote sensing image classification efficiency and accuracy, demonstrating potential advantages over classical methods in processing large Earth observation datasets.

Contribution

The paper presents a novel quanvolutional neural network approach for remote sensing data, reducing parameters and eliminating quantum kernel training, thus enhancing scalability and performance.

Findings

Achieved around 5% accuracy improvement in EO classification tasks.

Demonstrated effectiveness on MNIST and Fashion MNIST datasets.

Reduced model complexity with fewer parameters and no need for quantum kernel training.

Abstract

A significant amount of remotely sensed data is generated daily by many Earth observation (EO) spaceborne and airborne sensors over different countries of our planet. Different applications use those data, such as natural hazard monitoring, global climate change, urban planning, and more. Many challenges are brought by the use of these big data in the context of remote sensing applications. In recent years, employment of machine learning (ML) and deep learning (DL)-based algorithms have allowed a more efficient use of these data but the issues in managing, processing, and efficiently exploiting them have even increased since classical computers have reached their limits. This article highlights a significant shift towards leveraging quantum computing techniques in processing large volumes of remote sensing data. The proposed Quanv4EO model introduces a quanvolution method for preprocessing multi-dimensional EO data. First its effectiveness is demonstrated through image classification tasks on MNIST and Fashion MNIST datasets, and later on, its capabilities on remote sensing image classification and filtering are shown. Key findings suggest that the proposed model not only maintains high precision in image classification but also shows improvements of around 5\% in EO use cases compared to classical approaches. Moreover, the proposed framework stands out for its reduced parameter size and the absence of training quantum kernels, enabling better scalability for processing massive datasets. These advancements underscore the promising potential of quantum computing in addressing the limitations of classical algorithms in remote sensing applications, offering a more efficient and effective alternative for image data classification and analysis.