Selective convolutional neural networks and cascade classifiers for remote sensing image classification

Training convolutional neural network (CNN) architecture fully, using pretrained CNNs as feature extractors, and fine-tuning pretrained CNNs on target datasets are three popular strategies used in state-of-the-art methods for remote sensing image classification. The full training strategy requires large-scale training dataset, whereas the fine-tuning strategy requires a pretrained model to resume network learning. In this study, we propose a new strategy based on selective CNNs and cascade classifiers to improve the classification accuracy of remote sensing images relative to single CNN. First, we conduct a comparative study of existing pretrained CNNs in terms of data augmentation and the use of fully connected layers. Second, selective CNNs, which based on class separability criterion, are presented to obtain an optimal combination from multiple pretrained models. Finally, classification accuracy is improved by introducing two-stage cascade linear classifiers, the prediction probability of which in the first stage is used as input for the second stage. Experiments on three public remote sensing datasets demonstrate the effectiveness of the proposed method in comparison with state-of-the-art methods.