Diversifying Deep Multiple Choices for Remote Sensing Scene Classification

Recently, deep models have shown powerful ability for remote sensing scene representation. However, the training process of these deep methods requires large amount of labelled samples while usual remote sensing image datasets cannot provide enough training samples. Therefore, the learned model is usually suboptimal. To solve the problem, this work focuses on obtaining multiple choices by training multiple models simultaneously, and then the human oracle can choose a proper one from these choices. However, training several models separately usually makes the obtained results similar. This paper tries to diversify the obtained choices by encouraging the obtained choices to repulse from each other. Experiments are conducted on Ucmerced Land Use dataset to validate the effectiveness of the proposed method to provide multiple diversified choices.