Pan-Sharpening via Causal-Aware Feature Distribution Calibration

In this work, we reveal an interesting observation within the multispectral modality: high-frequency components exhibit a long-tailed distribution, in contrast to the Gaussian distribution of dominant low-frequency components. This dual-distribution characteristic presents a challenge for network optimization, leading to overfitting on low-frequency information while neglecting essential high-frequency details. Addressing this issue from a causal inference perspective, we identify optimizer momentum as a confounding factor that biases models toward focusing on the head part of the high-frequency distribution during training. To counteract this effect, we propose a novel optimization strategy and supplement the global-modeling network architecture to balance the frequencies learning. In the training stage, we employ the recurrent weighted key-value (RWKV) architecture, which features a global receptive field, to effectively learn the long-tailed distribution of high-frequency components and quantify the cumulative direction of feature bias. During the testing stage, we apply counterfactual reasoning to adjust feature distributions based on the quantified bias. To our knowledge, this is the first time to investigate the imbalance of frequency learning within pan-sharpening from the causal inference perspective. Extensive experiments on three benchmark datasets demonstrate that our method significantly outperforms state-of-the-art approaches, showcasing its effectiveness and robustness in pan-sharpening tasks.