Confidence-Aware Selective Test-Time Adaptation for Remote-Sensing Pansharpening

Pansharpening aims to fuse low-resolution multispectral (LRMS) and panchromatic (PAN) images to generate high-resolution multispectral (HRMS) imagery. However, models trained on data from a specific sensor often generalize poorly to unseen sensors, resulting in significant performance degradation. Existing solutions can be categorized into full model retraining and zero-shot adaptation. The former requires substantial computational resources and labeled target-domain data, making it impractical for rapid deployment. The latter avoids retraining but typically suffers from considerable performance degradation under cross-sensor domain shifts. To address the issues, we propose a Confidence-Aware Selective Test-time Adaptation (CSTTA) framework for cross-sensor pansharpening. Specifically, test-time augmentation is employed to estimate prediction uncertainty and construct confidence-aware weighting maps, which guide adaptation toward more reliable regions. In addition, a multi-patch sensitivity-based parameter selection strategy is introduced to update only a small subset of sensor-sensitive parameters, thereby reducing optimization cost while maintaining adaptation effectiveness. Spatial, spectral, and output-consistency constraints are further incorporated to stabilize the adaptation process and balance structural preservation with spectral fidelity. Extensive experiments on multiple cross-sensor pansharpening benchmarks demonstrate that CSTTA consistently improves the performance of various backbone networks and achieves state-of-the-art results compared with existing transfer methods.