Self-Supervised Representation Learning for Remote Sensing Image Change Detection Based on Temporal Prediction

• Main Authors: Huihui Dong, Wenping Ma, Yue Wu, Jun Zhang, Licheng Jiao
• Format: Article
• Language: English
• Published: MDPI AG 2020-06-01
• Series: Remote Sensing
• Subjects: unsupervised change detection; generative adversarial networks; deep belief networks; self-supervised representation learning; remote sensing images
• Online Access: https://www.mdpi.com/2072-4292/12/11/1868

Traditional change detection (CD) methods operate in the simple image domain or hand-crafted features, which has less robustness to the inconsistencies (e.g., brightness and noise distribution, etc.) between bitemporal satellite images. Recently, deep learning techniques have reported compelling performance on robust feature learning. However, generating accurate semantic supervision that reveals real change information in satellite images still remains challenging, especially for manual annotation. To solve this problem, we propose a novel self-supervised representation learning method based on temporal prediction for remote sensing image CD. The main idea of our algorithm is to transform two satellite images into more consistent feature representations through a self-supervised mechanism without semantic supervision and any additional computations. Based on the transformed feature representations, a better difference image (DI) can be obtained, which reduces the propagated error of DI on the final detection result. In the self-supervised mechanism, the network is asked to identify different sample patches between two temporal images, namely, temporal prediction. By designing the network for the temporal prediction task to imitate the discriminator of generative adversarial networks, the distribution-aware feature representations are automatically captured and the result with powerful robustness can be acquired. Experimental results on real remote sensing data sets show the effectiveness and superiority of our method, improving the detection precision up to 0.94–35.49%.