Semi-Supervised Scene Classification for Remote Sensing Images Based on CNN and Ensemble Learning

The special characteristic of remote sensing (RS) images being large scale while only low number of labeled samples available in practical applications has been obstacle to the development of RS image classification. In this paper, a novel semi-supervised framework is proposed. The high-capacity convolutional neural networks (CNN) are adopted to extract preliminary image features. The strategy of ensemble learning is then utilized to establish discriminative image representations by exploring intrinsic information of available data. Plain supervised learning is finally performed to obtain classification results. To verify the efficacy of our work, we compare it with mainstream feature representation and semi-supervised approaches. Experimental results show that by utilizing CNN features and ensemble learning, our framework can obtain more effective image representations and achieve superior results compared with other paradigms of semi-supervised classification.