Supervised learning with convolutional neural networks for hyperspectral visualization

Abstract

ABSTRACTThe Visualization for hyperspectral images is demanded by the ground processing system for quick view and information survey. Previous methods using band selection or dimension reduction fail to produce stable details and good colours as reasonable as corresponding multispectral images. In this paper, a mixed strategy is proposed which utilizes multi-band selection and supervised learning to integrate natural spatial details within hyperspectral bands for visualization. In the training stage, multispectral images are introduced as the reference, which are targeted by convolutional neural networks modelling the conversion from hyperspectral bands to multispectral bands. In order to preserve the colour, the hyperspectral band numbers are recorded for those with high correlation to the reference images, which are put into the network for dimension reduction. The proposed method is tested for the EO-1 Hyperion hyperspectral images with LandSat-8 images as the benchmark. The results are compared with five state-of-the-art algorithms. The comparison results show that the present method has good performance in maintaining both structures and colours.