Self-paced stacked denoising autoencoders based on differential evolution for change detection

Due to the existence of speckle noise in synthetic aperture radar images, the traditional unsupervised change detection methods do not need any prior information whereas cannot preserve details well. In order to improve change detection performance, change detection methods exploiting supervised classifier have been investigated recently. These methods require reliable labeled samples to train a robust classifier and these samples are always unavailable for image change detection. In this paper, we put forward a novel self-paced stacked denoising autoencoders model to address this issue. In the proposed model, stacked denoising autoencoders are adopted as the supervised classifier, and then self-paced learning is employed to improve it. During iterations, each training sample is associated with a weight and stacked denoising autoencoders are implemented to learn these weighted samples. Furthermore, in the original self-paced learning, it is difficult to determine the pace parameter for acquiring the desired classification performance. Therefore differential evolution is employed to acquire an appropriate pace parameter sequence. Experiments on five real synthetic aperture radar image datasets demonstrate the feasibility and availability of the proposed model. Compared with several other change detection methods, the proposed model is more robust to the speckle noise and can achieve better performance on high resolution synthetic aperture radar images.