The convolutional neural network (CNN) is widely used for image classification because of its powerful feature extraction capability. The key challenge of CNN in remote sensing (RS) scene classification is that the size of data set is small and images in each category vary greatly in position and angle, while the spatial information will be lost in the pooling layers of CNN. Consequently, how to extract accurate and effective features is very important. To this end, we present a Siamese capsule network to address these issues. Firstly, we introduce capsules to extract the spatial information of the features so as to learn equivariant representations. Secondly, to improve the classification accuracy of the model on small data sets, the proposed model utilizes the structure of the Siamese network as embedded verification. Finally, the features learned through Capsule networks are regularized by a metric learning term to improve the robustness of our model. The effectiveness of the model on three benchmark RS data sets is verified by different experiments. Experimental results demonstrate that the comprehensive performance of the proposed method surpasses other existing methods.

卷积神经网络（CNN）具有强大的特征提取能力，因此被广泛用于图像分类。CNN在遥感（RS）场景分类中的主要挑战是数据集的大小很小，并且每个类别中的图像的位置和角度都存在很大差异，而空间信息将在CNN的池化层中丢失。因此，如何提取准确有效的特征非常重要。为此，我们提出了一个暹罗胶囊网络来解决这些问题。首先，我们引入胶囊来提取特征的空间信息，以学习等变表示。其次，为提高模型在小数据集上的分类精度，该模型利用暹罗网络的结构作为嵌入式验证。最后，通过Capsule网络学习的功能可以通过度量学习术语进行规范化，以提高模型的健壮性。通过不同的实验验证了该模型在三个基准RS数据集上的有效性。实验结果表明，该方法的综合性能优于其他现有方法。