STeInFormer enhances remote sensing change detection by interacting bi-temporal features during feature extraction and using fixed DCT frequency components as a parameter-light token mixer.
Multi- decadal mangrove forest change detection and prediction in honduras, central america, with landsat imagery and a markov chain model,
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STeInFormer: Spatial-Temporal Interaction Transformer Architecture for Remote Sensing Change Detection
STeInFormer enhances remote sensing change detection by interacting bi-temporal features during feature extraction and using fixed DCT frequency components as a parameter-light token mixer.