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.
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.
The existing models based on quantum pixels can mine spectral information and entanglement features, but are time-consuming in practice due to its characteristics on pixel-level entanglement. For this reason, an innovative image classification model, named ‘Quantum Superpixel Entanglement Classification (QSEC) model’, is proposed in this study. By treating superpixels in remote sensing images (RSIs) as quantum particles, we extend the paradigm of image classification from the pixel level to the superpixel level. This design fully leverages the spectral information, textural features, and inter-superpixel quantum entanglement characteristics inherent in RSIs, enabling the effective capture and processing of complex image information via quantum entanglement-driven computational frameworks. Furthermore, the framework quantizes RSI superpixels to map their greyscale and texture features onto corresponding quantum states, constructs a quantum superpixel entanglement pattern, extracts the entanglement relationships between superpixels using entanglement coefficients, and ultimately develops a classification method applicable to multispectral images. The results from the four groups of the experiments demonstrated that: (1) The QSEC model achieves an average classification accuracy of and Kappa coefficient (KC) of 92.02% and 0.86, respectively, with an average running time of 29.25 seconds; (2) the running time of the QSEC model was decreased by up to 400 seconds, the classification speed was improved by 75.29% when compared with the neural network classification (NNC), support vector machine (SVM) and deep learning (DL) model; and (3) the QSEC model improves classification accuracy by 8.76% and increases KC by 0.12 when compared with unsupervised classification models such as ISODATA and K-means.
Acceleration of image segmentation using deep learning methods on satellite imagery has become ubiquitous in various applications areas such as land cover classification, disaster monitoring and vegetation detection. However, the increase in satellite image resolution and large data volume required for remote sensing applications has resulted in a substantial increase in computational resource usage and demand for real-time processing. This paper investigates quantum computing as a novel approach to meet these computational demands, exploiting its parallel processing strengths. We evaluate hybrid quantum models (COQCNN, MQCNN, FQCNN) against classical CNN and U-Net architectures in remote sensing classification. Although COQCNN and MQCNN underperformed, FQCNN reached 53.26% accuracy, outperforming the classical CNN by 8%. Despite quicker convergence, quantum models struggle with complex feature segmentation, a task where U-Net excels. This study highlights quantum convolutions as a potential path to enhance convergence while addressing challenges like noise from multiple quantum channels affecting accuracy.
Image classification plays an important role in remote sensing. Earth observation (EO) has inevitably arrived in the big data era, but the high requirement on computation power has already become a bottleneck for analyzing large amounts of remote sensing data with sophisticated machine learning models. Exploiting quantum computing might contribute to a solution to tackle this challenge by leveraging quantum properties. This article introduces a hybrid quantum-classical convolutional neural network (QC-CNN) that applies quantum computing to effectively extract high-level critical features from EO data for classification purposes. Besides that, the adoption of the amplitude encoding technique reduces the required quantum bit resources. The complexity analysis indicates that the proposed model can accelerate the convolutional operation in comparison with its classical counterpart. The model's performance is evaluated with different EO benchmarks, including Overhead-MNIST, So2Sat LCZ42, PatternNet, RSI-CB256, and NaSC-TG2, through the TensorFlow Quantum platform, and it can achieve better performance than its classical counterpart and have higher generalizability, which verifies the validity of the QC-CNN model on EO data classification tasks.
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.
We demonstrate the application of a quantum feature extraction method to enhance multi-class image classification for space applications. By harnessing the dynamics of many-body spin Hamiltonians, the method generates expressive quantum features that, when combined with classical processing, lead to quantum-enhanced classification accuracy. Using a strong and well-established ResNet50 baseline, we achieved a maximum classical accuracy of 83%, which can be improved to 84% with a transfer learning approach. In contrast, applying our quantum-classical method the performance is increased to 87% accuracy, demonstrating a clear and reproducible improvement over robust classical approaches. Implemented on several of IBM's quantum processors, our hybrid quantum-classical approach delivers consistent gains of 2-3% in absolute accuracy. These results highlight the practical potential of current and near-term quantum processors in high-stakes, data-driven domains such as satellite imaging and remote sensing, while suggesting broader applicability in real-world machine learning tasks.
Earth observation (EO) has inevitably entered the Big Data era. The computational challenge associated with analyzing large EO data using sophisticated deep learning models has become a significant bottleneck. To address this challenge, there has been a growing interest in exploring quantum computing as a potential solution. However, the process of encoding EO data into quantum states for analysis potentially undermines the efficiency advantages gained from quantum computing. This article introduces a hybrid quantum deep learning model that effectively encodes and analyzes EO data for classification tasks. The proposed model uses an efficient encoding approach called superpixel encoding, which reduces the quantum resources required for large image representation by incorporating the concept of superpixels. To validate the effectiveness of our model, we conducted evaluations on multiple EO benchmarks, including Overhead-MNIST, So2Sat LCZ42, and SAT-6 datasets. In addition, we studied the impacts of different interaction gates and measurements on classification performance to guide model optimization. The experimental results suggest the validity of our model for accurate classification of EO data. Our models and code are available on https://github.com/zhu-xlab/SEQNN.