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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

How Quantum Computing-Friendly Multispectral Data can be?

Manish K. Gupta, Martin Beseda, Piotr Gawron

Published Jul 17, 2022• 13 citations
Publisher

Abstract

Quantum computers consisting of hundreds of noisy qubits are already available and can run specific quantum algorithms although a large-scale fully error-corrected quantum computer is decades away. It is important to study their application to real-life computational problems. One such problem is Land Use and Land Cover classification of Earth Observation data set collected from the earth observation satellite mission using quantum machine learning methods. In this work, we compare the performance of the classical neural network on the re-labeled dataset of the Copernicus Sentinel-2 mission, when the model has access to Projected Quantum Kernel features. We show that classical neural net-work training accuracy increases drastically when the model has access to Projected Quantum Kernel features. This study shows the potential for quantum machine learning methods to Earth Observation data and provides key evidence for further investigation.

DOI
10.1109/igarss46834.2022.9883676

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Quantum computers consisting of hundreds of noisy qubits are already available and can run specific quantum algorithms although a large-scale fully error-corrected quantum computer is decades away. It is important to study their application to real-life computational problems. One such problem is Land Use and Land Cover classification of Earth Observation data set collected from the earth observation satellite mission using quantum machine learning methods. In this work, we compare the performance of the classical neural network on the re-labeled dataset of the Copernicus Sentinel-2 mission, when the model has access to Projected Quantum Kernel features. We show that classical neural net-work training accuracy increases drastically when the model has access to Projected Quantum Kernel features. This study shows the potential for quantum machine learning methods to Earth Observation data and provides key evidence for further investigation.
Quantum computers with hundreds of noisy qubits are already available for the research community. They have the potential to run complex quantum computations well beyond the computational capacity of any classical device. It is natural to ask the question, what application these devices could be useful for? Land Use and Land Cover classification of multispectral Earth observation data collected from the earth observation satellite mission is one such problem that is hard for classical methods due to its unique characteristics. In this work, we compare the performance of several classical machine learning algorithms on the stilted re-labeled dataset of the Copernicus Sentinel-2 mission, when the algorithm has access to Projected Quantum Kernel (PQK) features. We show that the classification accuracy increases drastically when the model has access to PQK features. We then naively study the performance of these algorithms with and without access to PQK features on the original Copernicus Sentinel-2 mission data set. This study provides key evidence that shows the potential of quantum machine learning methods for Earth Observation data.
Exploiting machine learning techniques to automatically classify multispectral remote sensing imagery plays a significant role in deriving changes on the Earth’s surface. However, the computation power required to manage large Earth observation data and apply sophisticated machine learning models for this analysis purpose has become an intractable bottleneck. Leveraging quantum computing provides a possibility to tackle this challenge in the future. This article focuses on land cover classification by analyzing Sentinel-2 images with quantum computing. Two hybrid quantum-classical deep learning frameworks are proposed. Both models exploit quantum computing to extract features efficiently from multispectral images and classical computing for final classification. As proof of concept, numerical simulation results on the LCZ42 dataset through the TensorFlow Quantum platform verify our models' validity. The experiments indicate that our models can extract features more effectively compared with their classical counterparts, specifically, the convolutional neural network (CNN) model. Our models demonstrated improvements, with an average test accuracy increase of 4.5% and 3.3%, respectively, in comparison to the CNN model. In addition, our proposed models exhibit better transferability and robustness than CNN models.
Abstract Achieving practical applications of quantum machine learning (QML) for real-world scenarios remains challenging despite significant theoretical progress. This paper proposes a novel approach for classifying satellite images, a task of particular relevance to the earth observation industry, using QML techniques. Specifically, we focus on classifying images that contain solar panels, addressing a complex real-world classification problem. Our approach begins with classical pre-processing to reduce the dimensionality of the satellite image dataset. We then apply neural quantum kernels-quantum kernels derived from trained quantum neural networks-for classification. We evaluate several strategies within this framework, demonstrating results that are competitive with the best classical methods. Key findings include the robustness of or results and their scalability, with successful performance achieved up to 8 qubits.
Exploiting deep learning techniques to automatically analyze multi-spectral remote sensing imagery plays an essential role in urban land cover and land use classification. However, the computation power required to analyze large earth observation data with complex machine learning models for this task becomes an intractable bottleneck. Leveraging quantum computing might tackle this challenge. In this paper, we present two hybrid quantum-classical deep learning frameworks. They both exploit quantum computing to extract features from multi-spectral images efficiently and classical computing for final classification. The effectiveness of our models is verified with the LCZ42 dataset through the TensorFlow Quantum platform.
Land cover classification using remote sensing data is essential for environmental monitoring and agricultural management. This study evaluates the feasibility of applying a Variational Quantum Classifier (VQC) to distinguish soybean fields from forest formations using Sentinel-2 imagery. Spectral bands B2, B3, B4 (10 m resolution) and annual mean NDVI were used as input features. The quantum model, implemented in Qiskit’s simulator, was compared with a classical neural network trained on the same dataset. While the classical model achieved higher accuracy (above 90% for both classes), the quantum classifier demonstrated competitive performance, particularly for forest identification. The results highlight current limitations of quantum classifiers in complex agricultural landscapes, while indicating future potential as quantum hardware and algorithms evolve.
We present in this paper a method to compare scene classification accuracy of C-band Synthetic aperture radar (SAR) and optical images utilizing both classical and quantum computing algorithms. This REU study uses data from the Sentinel satellite. The dataset contains (i) synthetic aperture radar images collected from the Sentinel-1 satellite and (ii) optical images for the same area as the SAR images collected from the Sentinel-2 satellite. We examine classical neural networks to classify four classes of images. We then explore Quantum Convolutional Neural Networks and deep learning techniques in terms of their training and classification performance. A hybrid Quantum-classical model that is trained on the Sentinel1-2 dataset is proposed, and its performance is then compared against the classical model in terms of classification accuracy.