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Recently, convolutional neural networks (CNN) have obtained promising resultsin single-image SR for hyperspectral pansharpening. However, enhancing CNNs'representation ability with fewer parameters and a shorter prediction time is achallenging and critical task. In this paper, we propose a novel multi-spectralimage fusion method using a combination of the previously proposed 3D CNN modelVolumeNet and 2.5D texture transfer method using other modality high resolution(HR) images. Since a multi-spectral (MS) image consists of several bands andeach band is a 2D image slice, MS images can be seen as 3D data. Thus, we usethe previously proposed VolumeNet to fuse HR panchromatic (PAN) images andbicubic interpolated MS images. Because the proposed 3D VolumeNet caneffectively improve the accuracy by expanding the receptive field of the model,and due to its lightweight structure, we can achieve better performance againstthe existing method without purchasing a large number of remote sensing imagesfor training. In addition, VolumeNet can restore the high-frequency informationlost in the HR MR image as much as possible, reducing the difficulty of featureextraction in the following step: 2.5D texture transfer. As one of the latesttechnologies, deep learning-based texture transfer has been demonstrated toeffectively and efficiently improve the visual performance and qualityevaluation indicators of image reconstruction. Different from the texturetransfer processing of RGB image, we use HR PAN images as the reference imagesand perform texture transfer for each frequency band of MS images, which isnamed 2.5D texture transfer. The experimental results show that the proposedmethod outperforms the existing methods in terms of objective accuracyassessment, method efficiency, and visual subjective evaluation.

Efficient and Accurate Hyperspectral Pansharpening Using 3D VolumeNet and 2.5D Texture Transfer