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IFIP advances in information and communication technology

Quantum-DL Integration for Precise Remote Sensing Image Classification

Harshad Duchal, Selvin Salve, Alfiya Tamboli, Atharva Gosavi, Pradip P. Ghorpade

Published Jan 1, 2024• 0 citations
Publisher
DOI
10.1007/978-3-031-69986-3_34

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Remote sensing image classification is a fundamental task for Earth observation and environmental monitoring. However, conventional convolutional neural networks (CNNs) are limited by computational capacity and struggle to efficiently process the rapidly growing volume of remote sensing data. To address this limitation, we propose HAQCCN (Hybrid Asymmetric Quantum–Classical Convolutional Network), a novel hybrid architecture that integrates quantum computation into the classical convolutional framework through asymmetric quantum convolutional circuits. In HAQCCN, the asymmetric quantum circuits enable a limited number of qubits to process more classical data while maintaining excellent feature extraction capability. Experiments conducted on the IBM Qiskit platform using the Overhead-MNIST, PatternNet, and RSI-CB256 datasets demonstrate that HAQCCN outperforms conventional CNNs and existing quantum models. Furthermore, we systematically investigate the effects of encoding schemes, the number of quantum convolutional kernels, and the number of qubits on model performance, confirming the effectiveness and scalability of the proposed method for remote sensing image classification.
Remote sensing image (RSI) classification is key for extracting value from remote sensing data, aiding decision-making and resource management. Recent quantum pixel-based models excel at handling complex spectral and entanglement features but face high computational costs and lengthy processing due to quantum pixel entanglement, limiting large-scale and real-time applications. To address this, this study proposes a novel Quantum Many-Body Entanglement Classification model. This model first performs superpixel segmentation on RSIs, and then converts the segmented images into quantum states with quantum characteristics. Each superpixel corresponds to one quantum state, thereby enhancing the classification granularity from the pixel level to the superpixel level. Subsequently, the model conducts cluster analysis based on the entanglement coefficient. This paper takes Dongying, Shandong Province as the research area and uses Landsat 8 image data to conduct a comparative experiment between the proposed classification method and the traditional remote sensing classification method. The results show that the classification accuracy of this method meets the requirements of practical applications.