Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 33, Issue 9, Pages 4257-4270Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2021.3056238
Keywords
Remote sensing; Feature extraction; Optical sensors; Optical imaging; Radar polarimetry; Image segmentation; Decoding; Change detection; commonality autoencoder; convolutional autoencoder (CAE); deep neural networks (DNNs)
Categories
Funding
- National Natural Science Foundation of China [62036006]
- Australian Research Council [LP180100114, DP200102611]
- Australian Research Council [LP180100114, DP200102611] Funding Source: Australian Research Council
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An unsupervised change detection method is proposed using a convolutional autoencoder and a commonality autoencoder to extract common features in heterogeneous images, distinguishing changed and unchanged regions. Experimental results demonstrate the promising performance of this method compared to existing approaches.
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.
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