Commonality Autoencoder: Learning Common Features for Change Detection From Heterogeneous Images

• Published in: IEEE transaction on neural networks and learning systems Vol. 33; no. 9; pp. 4257 - 4270
• Main Authors: Wu, Yue, Li, Jiaheng, Yuan, Yongzhe, Qin, A. K., Miao, Qi-Guang, Gong, Mao-Guo
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-09-2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Change detection; Commonality; commonality autoencoder; convolutional autoencoder (CAE); Decoding; deep neural networks (DNNs); Feature extraction; Image segmentation; Optical imaging; Optical sensors; Radar imaging; Radar polarimetry; Remote sensing; Representations; Synthetic aperture radar
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2021.3056238

Change detection based on heterogeneous images, such as optical images and synthetic aperture radar images, is a challenging problem because of their huge appearance differences. To combat this problem, we propose an unsupervised change detection method that contains only a convolutional autoencoder (CAE) for feature extraction and the commonality autoencoder for commonalities exploration. The CAE can eliminate a large part of redundancies in two heterogeneous images and obtain more consistent feature representations. The proposed commonality autoencoder has the ability to discover common features of ground objects between two heterogeneous images by transforming one heterogeneous image representation into another. The unchanged regions with the same ground objects share much more common features than the changed regions. Therefore, the number of common features can indicate changed regions and unchanged regions, and then a difference map can be calculated. At last, the change detection result is generated by applying a segmentation algorithm to the difference map. In our method, the network parameters of the commonality autoencoder are learned by the relevance of unchanged regions instead of the labels. Our experimental results on five real data sets demonstrate the promising performance of the proposed framework compared with several existing approaches.