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Perspective Chapter: Quantum-Enhanced Hyperspectral Image Classification

Aparna Halbe, Sunil G. Bhirud, Greenal Tambe, Parikshit Patil, Varsha Hole, Nikahat Mulla

Published Jul 13, 2026• 0 citations
Publisher

Abstract

Hyperspectral image (HSI) classification is a high-dimensional learning problem central to remote sensing, precision agriculture, and land-cover analysis. This paper presents a rigorous comparative study of quantum-enhanced and classical machine learning pipelines applied to the Indian Pines benchmark dataset (145145 pixels, 200 spectral bands, 16 land-cover classes). We propose a Quantum Band Selection (QBS) strategy using parameterised PennyLane circuits, which replaces traditional PCA/LDA dimensionality reduction with a physically motivated spectral scoring mechanism based on parameterised quantum circuit interference. The top-30 QBS-selected bands are fed to a spatial-spectral CNN classifier achieving an Overall Accuracy (OA) of 99.71% and Cohen’s , while the same bands with a Random Forest baseline reach OA = 92.81% – – both outperforming all classical reduction strategies tested. A comprehensive benchmark across ten methods (SVM-RBF, k-NN, Logistic Regression, RF, PCA + RF, LDA + RF, MI + RF, Quantum Variational + RF, QBS + RF, and QBS + CNN) is reported with OA, Average Accuracy (AA), Cohen’s Kappa, and wall-clock timing. Noise sensitivity analysis under depolarising, bit-flip, and phase-flip models is conducted. The results demonstrate that quantum circuits can serve as effective preprocessing tools in the NISQ era without requiring a full quantum advantage over classical computation.

DOI
10.5772/intechopen.1016114

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arXiv:2410.18814•Oct 24, 2024•1 citation
Hyperspectral image (HSI) classification is a high-dimensional learning problem central to remote sensing, precision agriculture, and land-cover analysis. This paper presents a rigorous comparative study of quantum-enhanced and classical machine learning pipelines applied to the Indian Pines benchmark dataset (145145 pixels, 200 spectral bands, 16 land-cover classes). We propose a Quantum Band Selection (QBS) strategy using parameterised PennyLane circuits, which replaces traditional PCA/LDA dimensionality reduction with a physically motivated spectral scoring mechanism based on parameterised quantum circuit interference. The top-30 QBS-selected bands are fed to a spatial-spectral CNN classifier achieving an Overall Accuracy (OA) of 99.71% and Cohen’s , while the same bands with a Random Forest baseline reach OA = 92.81% – – both outperforming all classical reduction strategies tested. A comprehensive benchmark across ten methods (SVM-RBF, k-NN, Logistic Regression, RF, PCA + RF, LDA + RF, MI + RF, Quantum Variational + RF, QBS + RF, and QBS + CNN) is reported with OA, Average Accuracy (AA), Cohen’s Kappa, and wall-clock timing. Noise sensitivity analysis under depolarising, bit-flip, and phase-flip models is conducted. The results demonstrate that quantum circuits can serve as effective preprocessing tools in the NISQ era without requiring a full quantum advantage over classical computation.
Comprehensive spectrum information is provided by hyperspectral imaging and is necessary for data processing and remote sensing. Yet, efficient categorization is hampered by its great dimensionality and intricate spectral fingerprints. We present a novel method in this paper to tackle these issues by utilizing Quantum Machine Learning (QML), more especially Quantum Neural Networks (QNN). In order to maintain crucial spectral information while lowering data complexity, we use dimensionality reduction techniques like Principal Component Analysis (PCA) and Kernel PCA (KPCA). To develop Modified Quantum Neural Network (MQNN) models for multi-class classification, these reduced representations are coupled with conventional Neural Networks (NN). Tested on the Salinas, Pavia, and Indian Pines datasets, our findings show that QML approaches are more accurate when processing high-dimensional hyperspectral data, presenting new opportunities for use in environmental monitoring, agriculture, and urban planning.
Quantum kernel methods have emerged as a promising approach for leveraging high-dimensional feature spaces in machine learning, particularly in domains where classical kernel methods face scalability limitations. In this work, we present the first large-scale study of fidelity-quantum-kernel support vector machines for hyperspectral data classification without requiring heavy prior feature selection or dimensionality reduction. By simulating quantum kernels using tensor network contraction techniques and GPU acceleration, we overcome the computational bottlenecks traditionally associated with quantum models, achieving quadratic scaling O(n^2) in the number of qubits. Our approach enables the evaluation of quantum kernels on hyperspectral data with hundreds of spectral bands, aligning quantum feature spaces with real-world remote sensing applications. We provide an in-depth analysis of kernel bandwidth optimization, demonstrating its crucial role in mitigating exponential concentration effects and ensuring the model's ability to generalize. Experimental results on binary classification (Indian Pines and Methane Detection) and multiclass classification (Indian Pines) demonstrate that quantum kernels achieve competitive performance compared to a broad range of state-of-the-art classical baselines. As illustrative cases, on four 50-band splits selected from Indian Pines, the quantum model achieved a 78.0 pm6.2% accuracy for a binary classification task compared to 72.0 pm5.0% for the standard radial basis function (RBF) kernel. For a four-class classification task, the quantum kernel reached 83.3 pm3.1% accuracy, outperforming several state-of-the-art baselines. On five 75-band splits selected from the Methane Detection dataset, the quantum approach yielded 58.5\pm5.0% accuracy versus 55.1\pm2.5% for the classical counterpart...
Quantum kernel methods have emerged as a promising approach for leveraging high-dimensional feature spaces in machine learning, particularly in domains where classical kernel methods face scalability limitations. In this work, we present the first large-scale study of fidelity-quantum-kernel support vector machines for hyperspectral data classification without requiring heavy prior feature selection or dimensionality reduction. By simulating quantum kernels using tensor network contraction techniques and graphics processing unit acceleration, we overcome the computational bottlenecks traditionally associated with quantum models, achieving quadratic scaling O(n2)O(n^2)O(n2) in the number of qubits. Our approach enables the evaluation of quantum kernels on hyperspectral data with hundreds of spectral bands, aligning quantum feature spaces with real-world remote sensing applications. We provide an in-depth analysis of kernel bandwidth optimization, demonstrating its crucial role in mitigating exponential concentration effects and ensuring the model's ability to generalize. Experimental results on binary classification (Indian Pines and methane detection) and multiclass classification (Indian Pines) demonstrate that quantum kernels achieve competitive performance compared to a broad range of state-of-the-art classical baselines. As illustrative cases, on four 50-band splits selected from Indian Pines, the quantum model achieved a 78.0±6.2%78.0±6.2\%78.0±6.2% accuracy for a binary classification task compared to 72.0±5.0%72.0±5.0\%72.0±5.0% for the standard radial basis function kernel. For a four-class classification task, the quantum kernel reached 83.3±3.1%83.3±3.1\%83.3±3.1% accuracy, outperforming several state-of-the-art baselines. On five 75-band splits selected from the methane detection dataset, the quantum approach yielded 58.5±5.0%58.5±5.0\%58.5±5.0% accuracy versus 55.1±2.5%55.1±2.5\%55.1±2.5% for the classical counterpart. This study provides a foundation for future exploration of quantum machine learning in high-dimensional Earth observation tasks.
Hyperspectral images (HSIs) showing objects belonging to several distinct target classes are characterized by dozens of spectral bands being available. However, some of these spectral bands are redundant and/or noisy, and hence, selecting highly informative and trustworthy bands for each class is a vital step for classification and for saving internal storage space; then the selected bands are termed a highly informative spectral band subset. We use a mutual information (MI)-based method to select the spectral band subset of a given class and two additional binary quantum classifiers, namely a quantum boost (Qboost) and a quantum boost plus (Qboost-Plus) classifier, to classify a two-label dataset characterized by the selected band subset. We pose both our MI-based band subset selection problem and the binary quantum classifiers as a quadratic unconstrained binary optimization (QUBO) problem. Such a quadratic problem is solvable with the help of conventional optimization techniques. However, the QUBO problem is an NP-hard global optimization problem, and hence, it is worthwhile for applying a quantum annealer. Thus, we adapted our MI-based optimization problem for selecting highly informative bands for each class of a given HSI to be run on a D-Wave quantum annealer. After the selection of these highly informative bands for each class, we employ our binary quantum classifiers to a two-label dataset on the D-Wave quantum annealer. In addition, we provide a novel multilabel classifier exploiting an error-encoding output code when using our binary quantum classifiers. As a real-world dataset in Earth observation, we used the well-known AVIRIS HSI of Indian Pine, north-western Indiana, USA. We can demonstrate that the MI-based band subset selection problem can be run on a D-Wave quantum annealer that selects the highly informative spectral band subset for each target class in the Indian Pine HSI. We can also prove that our binary quantum classifiers and our novel multilabel classifier generate a correct two- and multilabel dataset characterized by their selected bands and with high accuracy such as having been produced by conventional classifiers-and even better in some instances.
Hyperspectral image (HSI) classification is a critical task in the field of remote sensing. Existing deep learning models have made significant progress in HSI classification, but often suffer from parameter redundancy and insufficient noise robustness when handling the high-dimensional nonlinear spatial-spectral information. Recently, quantum computing have garnered significant attention in modeling higher-order correlations with remarkable parameter efficiency. Inspired by this, we propose a QUantum-Enhanced nEural Network (QueenNet) for HSI classification. QueenNet comprises three primary modules, including Quantum Encoding (QE), Quantum Convolution (QC), and Quantum Measurement (QM). Specifically, QE maps HSI into an hyper high-dimensional Hilbert space, encoding it as a quantum states. QC performs HSI feature extraction and transformation using quantum circuits composed of parameterized quantum gate sequences, which effectively enhances the model's ability to capture joint spatial-spectral features while suppressing spectral noise. QM introduces nonlinear feature extraction operations and decodes the HSI features of quantum states back into classic interpretable probability distributions for classification. QueenNet effectively alleviate the limitations of existing HSI classification deep learning models by leveraging the high-dimensional feature representation advantages of quantum computing, introducing a novel paradigm to HSI classification. Extensive experiments on three benchmark datasets demonstrate that QueenNet achieves an order-of-magnitude reduction in model parameters while surpassing the classification performance of state-of-the-art methods.
In order to leverage quantum computers for machine learning tasks such as image classification, careful consideration is required: NISQ-era quantum computers have limitations, which include noise, scalability, read-in and read-out times, and gate operation times. Therefore, strategies should be devised to mitigate the impact that complex datasets can have on the overall efficiency of a quantum machine learning pipeline. This may otherwise lead to excessive resource demands or increased noise. We apply a classical feature extraction method using a ResNet10-inspired convolutional autoencoder to both reduce the dimensionality of the dataset and extract abstract and meaningful features before feeding them into a quantum machine learning block. The quantum block of choice is a quantum-enhanced support vector machine (QSVM), as support vector machines typically do not require large sample sizes to identify patterns in data and have short-depth quantum circuits, which limits the impact of noise. The autoencoder is trained to extract meaningful features through image reconstruction, aiming to minimize the mean squared error across a training set. Three image datasets are used to illustrate the pipeline: HTRU-1, MNIST, and CIFAR-10. We also include a quantum-enhanced one-class support vector machine (QOCSVM) for the highly unbalanced HTRU-1 set, as well as classical machine learning results to serve as a benchmark. Finally, the HTRU-2 dataset is also included to serve as a benchmark for a dataset with well-correlated features. The autoencoder achieved near-perfect reconstruction and high classification accuracy for MNIST, while CIFAR-10 showed poorer performance due to image complexity, and HTRU-1 struggled because of dataset imbalance. This highlights the need for a balance between dimensionality reduction through classical feature extraction and prediction performance using quantum methods.