An Introspective Learning Strategy for Remote Sensing Scene Classification

Jingran Su, Qi Wang, Shangdong Chen, Xuelong Li

Abstract: In this paper, a novel introspective learning strategy for remote sensing scene classification is proposed. Through this strategy, the neural network used for classification can introspectively generate negative samples. In most training deep neural networks, negative samples are rarely noticed. We are the first to actively introduce negative samples into the remote sensing scene classification tasks. The goal of this paper is to analyze the effect of introspective negative samples on remote sensing scene classification tasks. Experiments demonstrate that the introduction of negative samples in training can effectively improve the classification accuracy and robustness. In addition, we found that our method can effectively against invalid remote sensing images.