Hyperspectral Image Super-Resolution Classification with a Small Training Set Using Spectral Variation Extended Endmember Library

Classification has been one of the most important applications of hyperspectral images (HSIs) in the past decade, because of the outstanding discrimination among different classes ensured by abundant and detailed spectral information enclosed in HSIs. While the classification accuracy must be guaranteed by plenty of training samples, which is difficult to be satisfied in many practical cases. Meanwhile, because of its comparatively low spatial resolution, mixed pixels are widely existed in HSIs which makes subpixel level classification techniques more preferable rather than traditional pixel-level ones. A novel super-resolution classification method is proposed in this paper to deal with the two above mentioned problems in HSI classification, that is, limited number of training samples and widely existed mixed pixels. Specifically, spectral variation is considered to construct spectral variation extended endmember library, with which the abundance fractions for each class within a mixed pixel are estimated using collaborative representation. And finally, the classification result with higher spatial resolution is obtained with subpixel spatial attraction model based subpixel mapping. Simulative experiments are employed for validation and comparison. Experimental results illustrate that the newly proposed method is capable of producing super-resolution classification map of low resolution HSI with less misclassification.