DTEA: Degradation-Aware Taylor Expansion Approximation Network for Pansharpening

In remote sensing image pansharpening, the fundamental objective is to generate high-resolution multispectral (HRMS) image that preserves spectral integrity while enhancing spatial resolution. However, contemporary approaches lack the ability to perceive the quality of input images, thereby failing to preserve desired performance in complex real-world scenarios characterized by panchromatic (PAN) image degradation (e.g., noise contamination or sensor limitations). To address this problem, we present a novel degradation-aware Taylor expansion approximation (DTEA) network for pansharpening, where DTEA includes the following key procedures: Firstly, the PAN image is hierarchically decomposed into feature maps to represent the degradation information through a proposed Taylor expansion approximation network (TEANet). Next, a multi-level information fusion network (MIFNet) is employed to integrate these feature maps with LRMS images, yielding fused maps with rich spatial and spectral information. Finally, the fused map from each layer is utilized to synthesize the desired HRMS image through inverse Taylor expansion, thereby overcoming diverse information degradation. To rigorously evaluate the effectiveness of our DTEA network, we conduct a systematic performance analysis across PAN images with diverse qualities. Extensive quantitative and qualitative experiments on three datasets demonstrate that our method outperforms state-of-the-art approaches while exhibiting excellent generalization capability in real-world scenarios. Source code will be made publicly available on https://github.com/MysterYxby/DTEA.