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Globally-Aware Continuous-Time Redistribution Learning for RS Image Change Detection | IEEE Journals & Magazine | IEEE Xplore
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Globally-Aware Continuous-Time Redistribution Learning for RS Image Change Detection


Abstract:

Change detection (CD) based on deep learning has achieved excellent performance in recent years. However, these models exhibit limited capability in complete temporal mod...Show More

Abstract:

Change detection (CD) based on deep learning has achieved excellent performance in recent years. However, these models exhibit limited capability in complete temporal modeling or face problems with fine-grained spatial features being overshadowed by the temporal context. Pure CNN-based CD pipelines also struggle to establish long-range connections. In this article, a globally aware continuous-time redistribution network (GCRNet) is proposed for RSCD. First, a boundary extraction branch is designed to preserve the semantic invariance of objects within the same boundary. This is achieved by providing boundary attention to adaptively guide the integration of temporal and spatial information. Then, a globally aware operator (GAO) is developed to obtain global interaction features. GAO utilizes the convolution theorem, which combines the Fourier transform and inverse Fourier transform, achieving it with low computational costs. Finally, an adaptive feature redistribution (AFR) module is designed to increase the distance between positive and negative samples in the latent space with change perception. It alleviates the effects of the severe class imbalance issue. Experimental results demonstrate that our proposed GCRNet surpasses 13 state-of-the-art CD methods. It achieves F1-score 0.33%, 0.62%, 0.84%, 0.17%, and 1.54% higher than the second-best model on the LEVIR-CD, LEVIR-CD+, WHU, CDD, and DSIFN datasets. The code of GCRNet is available at https://github.com/XiaowenZhang-kuku/GCRNet.
Article Sequence Number: 4413115
Date of Publication: 03 September 2024

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