Remote Sensing Image Change Detection With Transformers

Modern change detection (CD) has achieved remarkable success by the powerful discriminative ability of deep convolutions. Based on a naive backbone (ResNet18) without sophisticated structures (e.g., feature pyramid network (FPN) and UNet), our model surpasses several advanced CD methods, including better than four recent attention-based methods in terms of efficiency and accuracy.

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• 1 However, high-resolution remote sensing CD remains challenging due to the complexity of objects in the scene.
• 2 Objects with the same semantic concept may show distinct spectral characteristics at different times and spatial locations.
• 3 Most recent CD pipelines using pure convolutions are still struggling to relate long-range concepts in space-time.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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