HyperPNN: Hyperspectral Pansharpening via Spectrally Predictive Convolutional Neural Networks

Abstract

Hyperspectral (HS) pansharpening intends to synthesize a HS image with a registered panchromatic image, to generate an enhanced image with simultaneous high spectral resolution and high spatial resolution. However, the spectral range gap between the two kinds of images and the need to resolve details for many continuous narrow bands make the technique prone to spectral distortion and spatial blurring. To mitigate the problems, we propose a new HS pansharpening framework via spectrally predictive convolutional neural networks (HyperPNN). In our proposed HyperPNN, spectrally predictive structure is introduced to strengthen the spectral prediction capability of a pansharpening network. Following the concept of the proposed HyperPNN, two specific pansharpening convolutional neural network (CNN) models, i.e., HyperPNN1 and HyperPNN2, are designed. Experimental results from three datasets suggest the excellent performance of our CNN-based HS pansharpening methods.