Zhang Zhouwei, Mi Xiaofei, Yang Jian, Wei Xiangqin, Liu Yan, Yan Jian, Liu Peizhuo, Gu Xingfa, Yu Tao
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.
National Engineering Laboratory for Satellite Remote Sensing Applications, Beijing 100094, China.
Sensors (Basel). 2023 Sep 21;23(18):8010. doi: 10.3390/s23188010.
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 network performance, a tensor quantum circuit is subsequently employed by tuning parameters on near-term quantum processors. We tested our models on the open-source RSI dataset. In our comparative study, we have concluded that the hybrid classical-quantum transferring CNN has achieved better performance than other pre-trained CNNs based RSISC methods with small training samples. Moreover, it has been proven that the proposed algorithm improves the classification accuracy while greatly decreasing the amount of model parameters and the sum of training data.
本研究的范围在于将预训练的卷积神经网络（CNN）与量子卷积神经网络（QCNN）相结合，应用于遥感图像场景分类（RSISC）。深度学习（RL）在遥感图像（RSI）分析中借助预训练的CNN实现了跨越式发展，并且预训练的CNN在遥感图像场景分类（RSISC）中表现出了卓越的性能。尽管如此，CNN的训练需要大量带注释的数据作为样本。当标记样本不足时，最常见的解决方案是使用预训练的CNN和大量自然图像数据集（例如，ImageNet）。然而，这些预训练的CNN需要大量带标签的数据进行训练，这在RSISC中往往不可行，尤其是当目标RSI具有与RGB自然图像不同的成像机制时。在本文中，我们提出了一种改进的混合经典 - 量子迁移学习CNN，它由经典和量子元素组成，用于对开源RSI数据集进行分类。该模型的经典部分由一个ResNet网络组成，该网络从RSI数据集中提取有用特征。为了进一步优化网络性能，随后通过在近期量子处理器上调整参数来使用张量量子电路。我们在开源RSI数据集上测试了我们的模型。在我们的比较研究中，我们得出结论，混合经典 - 量子迁移CNN在小训练样本的情况下，比其他基于预训练CNN的RSISC方法取得了更好的性能。此外，已经证明所提出的算法提高了分类准确率，同时大大减少了模型参数的数量和训练数据的总量。
