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IEEE Access

Image Fusion With Quantum Processing for Scene Classification Using the BigEarthNet Dataset

Leslie Miller, Glen S. Uehara, Niraj Anil Babar, Andreas Spanias

Published Jan 1, 2026• 1 citation
Publisher

Abstract

This paper presents algorithms, simulations, and results on the use of optical and Synthetic Aperture Radar (SAR) quantum image fusion and machine learning algorithms for radar and remote sensing applications. We propose novel quantum image fusion techniques that extract meaningful features for identifying and classifying scenes obtained from the BigEarthNet dataset. Specifically, this work focuses on multi-label scene classification using quantum image fusion of C-band SAR and optical imagery from the BigEarthNet dataset. The proposed fusion techniques enhance and sharpen structural and textural details that are not readily observable in raw SAR imagery, thereby improving downstream classification performance. To enable quantum image fusion, we design new quantum circuits that employ unitary and Pauli gates for phase encoding of pixel intensity values. These circuits allow optical and SAR image information to be encoded and fused within a quantum processing framework. Three fusion strategies are investigated, resulting in three distinct quantum image fusion models. Comparative evaluation of these models demonstrates that pixel-averaging–based quantum fusion yields improved classification accuracy relative to alternative fusion approaches. The fused images are subsequently processed using deep classical neural networks to perform scene classification. Our neural network study begins by customizing the Visual Geometry Group 16 (VGG16) architecture for multi-label remote sensing classification tasks. In addition, we examine the use of a Residual Network 50 (ResNet-50) architecture and provide comparative performance analysis between the two models. The effects of quantum noise on the fusion and classification pipeline are also studied to assess robustness under realistic quantum processing conditions. To evaluate classification performance, we employ standard metrics such as F1 and F2 scores, and additionally introduce false discovery rate and false negative rate metrics to provide a more comprehensive assessment of classification accuracy, particularly in multi-label settings. Overall, the results demonstrate the effectiveness of optical and SAR quantum image fusion in enhancing scene classification performance and highlight the potential of integrating quantum image processing with deep learning for advanced remote sensing applications.

DOI
10.1109/access.2026.3698715

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This paper presents algorithms, simulations, and results on the use of optical and Synthetic Aperture Radar (SAR) quantum image fusion and machine learning algorithms for radar and remote sensing applications. We propose novel quantum image fusion techniques that extract meaningful features for identifying and classifying scenes obtained from the BigEarthNet dataset. Specifically, this work focuses on multi-label scene classification using quantum image fusion of C-band SAR and optical imagery from the BigEarthNet dataset. The proposed fusion techniques enhance and sharpen structural and textural details that are not readily observable in raw SAR imagery, thereby improving downstream classification performance. To enable quantum image fusion, we design new quantum circuits that employ unitary and Pauli gates for phase encoding of pixel intensity values. These circuits allow optical and SAR image information to be encoded and fused within a quantum processing framework. Three fusion strategies are investigated, resulting in three distinct quantum image fusion models. Comparative evaluation of these models demonstrates that pixel-averaging–based quantum fusion yields improved classification accuracy relative to alternative fusion approaches. The fused images are subsequently processed using deep classical neural networks to perform scene classification. Our neural network study begins by customizing the Visual Geometry Group 16 (VGG16) architecture for multi-label remote sensing classification tasks. In addition, we examine the use of a Residual Network 50 (ResNet-50) architecture and provide comparative performance analysis between the two models. The effects of quantum noise on the fusion and classification pipeline are also studied to assess robustness under realistic quantum processing conditions. To evaluate classification performance, we employ standard metrics such as F1 and F2 scores, and additionally introduce false discovery rate and false negative rate metrics to provide a more comprehensive assessment of classification accuracy, particularly in multi-label settings. Overall, the results demonstrate the effectiveness of optical and SAR quantum image fusion in enhancing scene classification performance and highlight the potential of integrating quantum image processing with deep learning for advanced remote sensing applications.
This paper presents algorithms, simulations, and results using machine learning and quantum image fusion algorithms for radar and remote sensing applications. Previous efforts in the classification of synthetic aperture radar (SAR) images using quantum machine learning provided encouraging results but, nevertheless modest accuracy. In this paper, we propose a novel quantum image fusion technique used for identifying and classifying objects obtained from C-band SAR and optical images. More specifically, we design a four-qubit quantum circuit to process the SAR image dataset. This method enhances the spectral details otherwise not seen when using the raw SAR dataset. In addition to the quantum circuit, we design deep neural networks (NN) to improve classification results. The Visual Geometry Group 16 (VGG16), a convolutional neural network that is sixteen layers deep, is customized and used for classification. The merit of quantum fusion as well as the promising results in improving the overall system and the potential of lowering size, weight, power, and cost (SWaP-C) is described.
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Remote sensing imagery classification remains a formidable challenge, primarily attributed to the high dimensionality and complexity inherent in the data. While deep learning has demonstrated efficacy in this domain, its computational demands and reliance on extensive training data pose significant hurdles. This research investigates how quantum computing could be able to overcome these constraints and improve deep learning’s capacity for classifying remote sensing data. The study provides an extensive review of recent developments in quantum deep learning techniques specifically tailored for remote sensing applications. Various quantum algorithms designed for image feature extraction and classification are discussed, shedding light on their potential to revolutionize the field. As a practical demonstration, the chapter presents a case study focused on leveraging quantum deep learning for land cover classification using Sentinel-2 imagery. This chapter’s main focus is on hybrid quantum convolutional neural networks (QCNNs) based on circuits as effective tools for remote sensing picture classification. These hybrid models integrate quantum layers within traditional neural networks, introducing a novel paradigm for image analysis. To evaluate the efficacy of this approach, the proposed QCNN architecture is applied to land-use and land-cover classification, employing the Euro SAT dataset as a benchmark.
Image classification has become a fundamental task in computer vision with applications in areas such as medical imaging, agriculture, environmental monitoring, and automated surveillance. Traditional machine learning techniques have achieved reasonable performance in classification tasks; however, they often struggle when dealing with high-dimensional and complex image datasets. Deep learning models, particularly Convolutional Neural Networks (CNNs), have significantly improved image classification performance by automatically learning hierarchical feature representations. Despite these advancements, classical deep learning models may still face challenges related to computational complexity and large-scale data processing.In recent years, quantum machine learning has emerged as a promising paradigm that combines principles of quantum computing with classical machine learning techniques to enhance computational efficiency and model performance. This study proposes a hybrid quantum–classical framework for image classification that integrates a deep residual network (ResNet-50) with a Quantum Support Vector Machine (QSVM). The ResNet-50 model is employed as a feature extraction mechanism to capture high-level visual representations from image data. The extracted features are then reduced in dimensionality using Principal Component Analysis (PCA) to simplify the feature space and improve computational efficiency.The reduced feature vectors are subsequently classified using a QSVM model that utilizes quantum feature maps to encode classical data into quantum states. Various quantum feature maps are explored to evaluate their impact on classification performance. Experimental results demonstrate that the hybrid quantum–classical approach achieves higher classification accuracy compared to conventional machine learning models such as Support Vector Machines and Random Forest classifiers. The proposed framework highlights the potential of combining classical deep learning architectures with quantum machine learning algorithms to address complex image classification challenges. This hybrid approach provides an efficient and scalable solution for advanced image analysis tasks and demonstrates the growing potential of quantum computing in artificial intelligence applications.
The field of quantum computing, especially quantum machine learning (QML), has been the subject of much research in recent years. Leveraging the quantum properties of superposition and entanglement promises exponential decrease in computation costs. With the promises of increased speed and accuracy in the quantum paradigm, many classical machine learning algorithms have been adapted to run on quantum computers, typically using a quantum-classical hybrid model. While some work has been done to compare classical and quantum classification algorithms in the Electro-Optical (EO) image domain, this paper will compare the performance of classical and quantum-hybrid classification algorithms in their applications on Synthetic Aperture Radar (SAR) data using the MSTAR dataset. We find that there is no significant difference in classification performance when training with quantum algorithms in ideal simulators as compared to their classical counterparts. However, the true performance benefits will become more apparent as the hardware matures.
Synthetic Aperture Radar (SAR) imaging is crucial in remote sensing due to its ability to produce high-resolution images regardless of weather conditions or daylight. However, SAR images often suffer from various types of noise, especially speckle noise, which degrades image quality and complicates data analysis. Traditional noise reduction techniques face challenges in balancing noise suppression with the preservation of image details. Recent deep learning approaches, such as U-Net architectures, have made strides in addressing these issues. Simultaneously, quantum computing has emerged as a promising field that can potentially enhance computational methods in image processing. We propose a hybrid classical-quantum U-Net framework that integrates quantum-classical algorithms with tensor networks for improved noise reduction in SAR images. Leveraging the representational capacity of tensor networks and the computational strengths of quantum algorithms, our method aims to surpass the limitations of classical techniques in managing complex noise patterns. In our architecture, certain layers of the U-Net are implemented using quantum circuits specifically optimized for noise suppression. The inclusion of tensor networks enables efficient handling of high-dimensional data within the hybrid model. The idea is to perform extensive experiments on standard SAR datasets to demonstrate that our hybrid U-Net outperforms traditional denoising methods and purely classical deep learning models in both noise reduction and detail preservation. Quantitative assessments using metrics like Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) would be used for comparison. Moreover, we will evaluate the computational efficiency of our approach, underscoring the potential of quantum computing to expedite complex image processing tasks. This work aims to highlight the feasibility and benefits of integrating quantum computing into practical remote sensing applications. Our hybrid quantum-classical U-Net architecture with tensor networks paves the way for advanced noise reduction techniques in SAR imaging and potentially other domains. Future research will focus on optimizing quantum circuit designs for specific noise types and exploring the scalability of our approach alongside advancements in quantum hardware.