Physics-informed Neural Operator for Pansharpening

Over the past decades, pansharpening has contributed greatly to numerous remote sensing applications, with methods evolving from theoretically grounded models to deep learning approaches and their hybrids. Though promising, existing methods rarely address pansharpening through the lens of underlying physical imaging processes. In this work, we revisit the spectral imaging mechanism and propose a novel physics-informed neural operator framework for pansharpening, termed PINO, which faithfully models the end-to-end electro-optical sensor process. Specifically, PINO operates as: (1) First, a spatial-spectral encoder is introduced to aggregate multi-granularity high-resolution panchromatic (PAN) and low-resolution multispectral (LRMS) features. (2) Subsequently, an iterative neural integral process utilizes these fused spatial-spectral characteristics to learn a continuous radiance field L i (x, y, λ) over spatial coordinates and wavelength, effectively emulating band-wise spectral integration. (3) Finally, the learned radiance field is modulated by the sensor's spectral responsivity R b (λ) to produce the desired fusion products. This physics-grounded fusion paradigm offers a principled solution for pansharpening in accordance with sensor imaging physics. Experiments on multiple benchmark datasets show that our method surpasses state-of-the-art fusion algorithms, achieving reduced spectral aberrations and finer spatial textures. Furthermore, extension to hyperspectral (HS) data demonstrates its generalizability and universality. The code is available at https://github.com/ez4lionky/PINO.