FUSION OF HYPERSPECTRAL AND PANCHROMATIC IMAGES: A HYBRID USE OF INDUSION AND NONLINEAR PCA

Generally, for optical satellite sensors spatial and spectral resolutions are highly correlated factors. In fact, given the design constraints of these sensors, there is an inverse relation between their spatial and spectral resolution. Thus, the hyperspectral sensors have a high spectral resolution i.e. large number of bands covering the electromagnetic spectrum, but a lower spatial resolution. On the other hand, panchromatic (PAN) images have the highest spatial resolution but no spectral diversity. For better utilization and interpretation, hyperspectral images having both high spectral and spatial resolution are desired. This can be achieved by making use of a high spatial resolution PAN image in the context of pansharpening or image fusion. Several fusion approaches have been proposed in the literature. In this paper we propose the use of a hybrid algorithm combining substitution and injection methods. One of the main challenges in hyperspectral image fusion is the improvement of the spatial resolution, i.e. spatial details while preserving the original spectral information. This requires addition of pertinent spatial details to each band of the HS image. However, due to large number of bands the pansharpening of HS images is computationally expensive. Thus a dimensionality reduction preprocess, compressing the original number of measurements into a lower dimensional space, becomes mandatory. In this paper we propose the use of non-linear principal components instead of the original HS bands as input to a fusion process to enhance the spatial resolution of the HS image.