A CNN-Based Model for Pansharpening of WorldView-3 Images

Fusing a multispectral image with a co-registered higher resolution single panchromatic band, provided by any multiresolution satellite systems, to rise the resolution of the former to that of the latter is known as pansharpening, and can be regarded as a guided super-resolution problem. Recently the use of convolutional neural networks (CNNs) has been extended to the pansharpening problem achieving state-of-the-art performance. Following this research line, the objective of this work was two-fold: provide a trained CNN model fitted to a specific sensor (WorldView-3) and explore a range of architectural configurations varied in both width and depth, seeking for the optimal one. Numerical and visual results show that the proposed solution compares favourably against reference methods.