Deep self-learning network for adaptive pansharpening

Abstract

Deep learning (DL)-based paradigms have recently made many advances in image pansharpening. However, most of the existing methods directly downscale the multispectral (MSI) and panchromatic (PAN) images with default blur kernel to construct the training set, which will lead to the deteriorative results when the real image does not obey this degradation. In this paper, a deep self-learning (DSL) network is proposed for adaptive image pansharpening. First, rather than using the fixed blur kernel, a point spread function (PSF) estimation algorithm is proposed to obtain the blur kernel of the MSI. Second, an edge-detection-based pixel-to-pixel image registration method is designed to recover the local misalignments between MSI and PAN. Third, the original data is downscaled by the estimated PSF and the pansharpening network is trained in the down-sampled domain. The high-resolution result can be finally predicted by the trained DSL network using the original MSI and PAN. Extensive experiments on three images collected by different satellites prove the superiority of our DSL technique, compared with some state-of-the-art approaches.