Stacked Fisher autoencoder for SAR change detection

•The original SAE is expanded to suit with the multiplicative noise in SAR change detection.•The features extracted by SFAE are more discriminative than the original stacked autoencoder due to that Fisher discriminant criterion is incorporated into SFAE.•Experiments on the simulated and real SAR dat...

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Bibliographic Details
Published in:	Pattern recognition Vol. 96; p. 106971
Main Authors:	Liu, Ganchao, Li, Lingling, Jiao, Licheng, Dong, Yongsheng, Li, Xuelong
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.12.2019
Subjects:	Change detection Fisher criterion Stacked autoencoder (SAE) Stacked fisher autoencoder (SFAE) Synthetic aperture radar (SAR) Stacked fisher autoencoder (SFAE) Stacked autoencoder (SAE) Synthetic aperture radar (SAR) Change detection Fisher criterion
ISSN:	0031-3203, 1873-5142
Online Access:	Get full text
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Summary:	•The original SAE is expanded to suit with the multiplicative noise in SAR change detection.•The features extracted by SFAE are more discriminative than the original stacked autoencoder due to that Fisher discriminant criterion is incorporated into SFAE.•Experiments on the simulated and real SAR datasets reveal that the proposed SFAE algorithm is effective on multitemporal single/multi-polarization SAR change detection. Specifically, the proposed SFAE method is obviously superior to the real-time methods on detection accuracy and the non-realtime methods on computational complexity. 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.
ISSN:	0031-3203 1873-5142
DOI:	10.1016/j.patcog.2019.106971