Modeling Temporal Difference Cancellation for Remote Sensing Change Detection

In binary remote sensing change detection, absolute feature differences discard sign patterns that influence how neighboring responses combine during spatial interpolation. Local differences with identical magnitudes can therefore produce different reconstructed responses. We propose a representation of temporal differences that accounts for this cancellation during spatial reconstruction. A shared hierarchical encoder and a shared projection produce signed differences between the two temporal feature maps. We compute a cancellation descriptor by subtracting the absolute value of the interpolated signed difference from the interpolated absolute difference. For interpolation weights that are nonnegative and sum to one, the descriptor is nonnegative, bounded above by the interpolated absolute difference, and invariant to exchanging the two input images. It captures local cancellation information that absolute differences alone do not encode. Lightweight residual readouts jointly process difference magnitude and cancellation at two reconstruction resolutions. Their outputs feed into the decoder alongside the original temporal feature pathway, allowing the decoder to learn how cancellation contributes to change prediction. The model is trained end to end with binary cross-entropy and Dice losses, requiring no additional task-specific annotations or auxiliary objectives.