Two Stages Pan-Sharpening Details Injection Approach Based on Very Deep Residual Networks

Pan-sharpening is a fusion task, which aims to combine a low spatial resolution multispectral (MS) image with a high spatial resolution single band panchromatic (PAN) image to produce a high spatial and spectral Pan-sharpened image. The success of a Pan-sharpening technique depends on its ability to boost the spatial quality of the MS image while preserving its spectral feature. To this end, we propose in this article a new two-stage detail injection approach allowing to reconstruct fine structures based on convolutional neural networks (CNNs). First, generalized Laplacian pyramid gain injections CNN is performed to estimate the optimal values of the injection gains for each MS band to inject spatial details extracted from the PAN image. Next, the result is enhanced by injecting the details missing using the power of deep residual learning. The quantitative and qualitative results on data sets from different satellites show that the proposed approach can achieve higher performances in both spatial and spectral qualities compared to the state of the art as well as the new CNN-based methods.