Sensors, Vol. 23, Pages 8010: Remote Sensing Image Scene Classification in Hybrid Classical & ndash;Quantum Transferring CNN with Small Samples

Sensors, Vol. 23, Pages 8010: Remote Sensing Image Scene Classification in Hybrid Classical–Quantum Transferring CNN with Small Samples Sensors doi: 10.3390/s23188010 Authors: Zhouwei Zhang Xiaofei Mi Jian Yang Xiangqin Wei Yan Liu Jian Yan Peizhuo Liu Xingfa Gu Tao Yu The scope of this research lies in the combination of pre-trained Convolutional Neural Networks (CNNs) and Quantum Convolutional Neural Networks (QCNN) in application to Remote Sensing Image Scene Classification(RSISC). Deep learning (RL) is improving by leaps and bounds pretrained CNNs in Remote Sensing Image (RSI) analysis, and pre-trained CNNs have shown remarkable performance in remote sensing image scene classification (RSISC). Nonetheless, CNNs training require massive, annotated data as samples. When labeled samples are not sufficient, the most common solution is using pre-trained CNNs with a great deal of natural image datasets (e.g., ImageNet). However, these pre-trained CNNs require a large quantity of labelled data for training, which is often not feasible in RSISC, especially when the target RSIs have different imaging mechanisms from RGB natural images. In this paper, we proposed an improved hybrid classical–quantum transfer learning CNNs composed of classical and quantum elements to classify open-source RSI dataset. The classical part of the model is made up of a ResNet network which extracts useful features from RSI datasets. To further refine the n...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research