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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Earth Observation Data Classification with Quantum-Classical Convolutional Neural Network

Fan Fan, Yilei Shi, Xiao Xiang Zhu

Published Jul 17, 2022• 10 citations
Publisher

Abstract

Due to the rapid growth of earth observation (EO) data and the complexity of machine learning models, the high requirement on the computation power for EO data analysis becomes a bottleneck. Exploiting quantum computing might tackle this challenge in the future. In this paper, we present a hybrid quantum-classical convolutional neural network (QC-CNN) to classify EO data which can accelerate feature extraction compared with its classical counterpart and handle multi-category classification tasks with reduced quantum resources. The model's validity is verified with the Overhead-MNIST dataset through the TensorFlow Quantum platform.

DOI
10.1109/igarss46834.2022.9883949

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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.
Abstract Quantum machine learning (QML) has gained increasing attention as a potential framework to address certain data analysis challenges in the future. Earth observation (EO) has entered the era of Big Data, where increasingly sophisticated deep learning models often require substantial computational resources for EO data analysis. Motivated by this, we explore the potential of hybrid quantum-classical learning for EO data classification despite the current limitations of quantum devices. This paper presents a hybrid model that incorporates multitask learning to assist efficient data encoding and employs a location weight module together with quantum convolution operations to extract valid features for classification. The validity of our proposed model was evaluated using multiple simplified EO benchmark settings as a proof-of-concept. Additionally, we experimentally investigated the robustness of the proposed model under reduced-sample and class-imbalanced conditions, providing insights into the behavior of hybrid QML models for EO data analysis.
Quantum machine learning (QML) has gained increasing attention as a potential solution to address the challenges of computation requirements in the future. Earth observation (EO) has entered the era of Big Data, and the computational demands for effectively analyzing large EO data with complex deep learning models have become a bottleneck. Motivated by this, we aim to leverage quantum computing for EO data classification and explore its advantages despite the current limitations of quantum devices. This paper presents a hybrid model that incorporates multitask learning to assist efficient data encoding and employs a location weight module with quantum convolution operations to extract valid features for classification. The validity of our proposed model was evaluated using multiple EO benchmarks. Additionally, we experimentally explored the generalizability of our model and investigated the factors contributing to its advantage, highlighting the potential of QML in EO data analysis.
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.
This article aims to investigate how circuit-based hybrid quantum convolutional neural networks (QCNNs) can be successfully employed as image classifiers in the context of remote sensing. The hybrid QCNNs enrich the classical architecture of convolutional neural networks by introducing a quantum layer within a standard neural network. The novel QCNN proposed in this work is applied to the land-use and land-cover classification, chosen as an Earth observation (EO) use case, and tested on the EuroSAT dataset used as the reference benchmark. The results of the multiclass classification prove the effectiveness of the presented approach by demonstrating that the QCNN performances are higher than the classical counterparts. Moreover, investigation of various quantum circuits shows that the ones exploiting quantum entanglement achieve the best classification scores. This study underlines the potentialities of applying quantum computing to an EO case study and provides the theoretical and experimental background for future investigations.
Urban land cover classification aims to derive crucial information from earth observation data and categorize it into specific land uses. To achieve accurate classification, sophisticated machine learning models trained with large earth observation data are employed, but the required computation power has become a bottleneck. Quantum computing might tackle this challenge in the future. However, representing images into quantum states for analysis with quantum computing is challenging due to the high demand for quantum resources. To tackle this challenge, we propose a hybrid quantum neural network that can effectively represent and classify remote sensing imagery with reduced quantum resources. Our model was evaluated on the Local Climate Zone (LCZ)-based land cover classification task using the TensorFlow Quantum platform, and the experimental results indicate its validity for accurate urban land cover classification.