Earth Observation Data Classification with Quantum-Classical Convolutional Neural Network
Fan Fan, Yilei Shi, Xiao Xiang Zhu · IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium · 2022
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.