LAUSR

Abstract Fusion of panchromatic (PAN) and multispectral (MS) images, called pansharpening, is one of the main challenging problems in remote sensing. Convolutional neural network (CNN) based pansharpening has been introduced in some works recently. The represented frameworks often use a multi-layer CNN for fusion of MS and PAN images. Limited number of training samples and the high number of network weights to be determined may cause overfitting problem. To deal with this difficulty, a simple structure comprised from two single layer convolutional networks is proposed in this paper. The shortage of deleted layers is compensated by applying 3D Gabor filters and shearlet transform in addition to nonlinear kernel based principal component analysis. The proposed framework not only has a simple structure, but also is superior to state-of-the-art CNN based pansharpening methods in terms of various quality assessment measures.