Interclass Similarity Transfer for Imbalanced Aerial Scene Classification

Abstract

Imbalanced class distributions widely exist in real-world aerial images, which brings a significant challenge to aerial scene classification due to the undesirable bias towards the majority classes as well as overfitting for the minority classes. Although the similarity between different scene classes may be inconsistent, they can be measured by the mean of feature statistics. This motivates us to transfer the statistics of the majority class to the minority class having similar feature statistics. Specifically, based on the observation that the feature statistic of each class may follow the Gaussian distribution, the similarity across different classes would thus be described by the mean of feature statistics. The distributions of minority classes would afterward be calibrated by statistical transfer via interclass similarity, and a sufficient number of features could hence be generated for the minority class to improve its performance in classifier learning. We demonstrate the effectiveness of the proposed method for imbalanced aerial scene classification on the imbalanced AID and NWPU-RESISC45 datasets. The proposed method outperforms alternatives by a large margin in both overall performance and minority classification performance of imbalanced aerial scenes.