Spatial-Spectral Bidirectional-Driven Collaborative Network with Coordinate-Aware and Spectral-Modulated Interaction...

High-resolution hyperspectral computational imaging is critical for applications such as environmental monitoring, urban planning, and precision agriculture. In practical hyperspectral imaging systems, physical hardware constraints inevitably lead to coupled degradations across spatial and spectral dimensions, making it difficult to simultaneously achieve high spatial resolution and high spectral fidelity. As a representative and widely studied hyperspectral computational imaging task, hyperspectral pansharpening aims to reconstruct high-resolution hyperspectral images by integrating low-resolution hyperspectral images with high-resolution panchromatic images. Existing methods frequently suffer from spectral distortion or blurred spatial details due to unidirectional fusion strategies or isolated processing branches that inadequately model the intrinsic spatial-spectral coupling in the imaging process. To overcome these limitations, we propose a bidirectional driving framework that enables synergistic mutual guidance between spatial detail infusion and spectral fidelity preservation. Specifically, spatial coordinate-aware representations are dynamically integrated into a spectral self-attention module, while spectral importance scores are utilized to modulate multi-receptive-field convolutions via channel-wise weighting. This bidirectional interaction mechanism forms a closed-loop coupling between spatial and spectral representations, ensuring enhanced spatial reconstruction while rigorously preserving spectral integrity. Furthermore, to bridge the gap between simulated experiments and real-world applications, we constructed a large-scale dataset derived from the ZY-1-02D satellite. This dataset features high-fidelity PAN (17,820 × 16,128) and HSI (1485 × 1344) pairs, which we have made publicly available to the community to facilitate future research. Extensive experiments on both benchmark simulations and the proposed ZY-1-02D dataset demonstrate that our method achieves state-of-the-art performance in both spatial fidelity and spectral preservation.