DIVERSIFIED DEEP STRUCTURAL METRIC LEARNING FOR LAND USE CLASSIFICATION IN REMOTE SENSING IMAGES

In this work, a diversified deep structural metric learning is proposed for remote sensing image classification. Firstly, a deep structural metric learning is introduced to take full advantage of structural information of training batches. Secondly, we impose a diversity regularization over the factors of deep structural metric learning to encourage them to be uncorrelated, such that each factor tends to model unique information during the training phase and all factors sums up to capture a larger proportion of information. The diversified model could benefit the classification of remote sensing images. Experiments are conducted on two real-world remote sensing image datasets to evaluate the effectiveness and wide applicability of the proposed approach. The results show that our proposed method can obtain comparable or even better results on remote sensing image classification when compared with the recent results.