Stacked Fisher autoencoder for SAR change detection

Abstract Stacked autoencoder is effective in image denoising and classification when it is used for synthetic aperture radar (SAR) change detection. However, the resulting features may not be discriminative enough for in some sense. To alleviate this problem, in this paper we propose a stacked Fisher autoencoder (SFAE) for SAR change detection. Specifically, in the framework of SFAE, unsupervised layer-wise feature learning and supervised fine-tuning are jointly performed when training the network. The trained network can be used to detect the changes in both of the single and multi-polarization SAR datasets in real-time. The proposed SFAE has two advantages. The first one is to expand the stacked autoencoder to suit the environment with the multiplicative noise in SAR change detection. The second is that the features extracted by SFAE are more discriminative than the original stacked autoencoder due to that Fisher discriminant criterion is incorporated into SFAE. The results on the simulated and real SAR datasets indicate that the proposed SFAE algorithm has a significant advantage on multitemporal single/multi-polarization SAR (SAR/PolSAR) change detection.