Hyperspectral Pansharpening Based on Guided Filter and Deep Residual Learning

Recently, deep learning technology has gained impressive effectiveness in the field of hyperspectral pansharpening. However, the existing methods with relatively shallow architectures ignores the deep features of hyperspectral image (HSI) and panchromatic (PAN) image, which leads to a limitation of the fusion performance. To address this issue, a novel hyperspectral pansharpening framework based on guided filter and deep residual learning is proposed in this paper. The proposed framework mainly consists of two parts: generating the initial HSI through enhancing spatial information while preserving the original spectral information, and mapping the residuals between the initialized HSI and the reference HSI for further improvement of the fusion accuracy. Experimental results demonstrate that the proposed framework can achieve superior fusion accuracy compared with other state-of-the-art hyperspectral pansharpening methods while providing better edge information.