Label Embedding Based on Interclass Correlation Mining for Remote Sensing Image Scene Classification

Remote sensing scene classification is an important, yet challenging task of remote sensing image interpretation. In recent years, the development of convolutional neural networks (CNNs) has significantly improved the accuracy of this study. However, most of the methods based on CNNs use discrete label representation, which ignores the potential correlations between scenes, resulting in insufficient generalization ability of the model. This letter proposes a model based on interclass association information mining, named correlation label embedding network (CLENet). Specifically, we mine interclass association information from the network’s predictions and continuously adjust them by backpropagation during the network training process to obtain continuous label representations. Unlike other methods, we distill the potential correlations between scenes as the supervisory signal of the network to guide the feature selection process of the network. This forces the output of the network to learn more scene-related features to adapt to complex application scenarios and improve the generalization performance of the model. Additionally, to enhance the discriminative nature of the model, we design regular metric terms based on the learned interclass association information. Compared with the state-of-the-art scene classification methods, the experimental results validate the potential of CLENet models on remote sensing image scene classification.