Abstract

The low spatial resolution of hyperspectral (HS) images generally limits the classification accuracy. Therefore, different multiresolution data fusion techniques have been proposed in the literature. In this paper, a method for supervised classification of spectral images from data fusion measurements is proposed. Specifically, the proposed approach exploits the spatial information of an RGB image by grouping pixels with similar characteristics into superpixels and fuses such features with the spectral information of an HS image. Simulations results on three datasets show that the proposed classification method improves the overall accuracy and reduces the computational complexity compared to the traditional approach that first performs the fusion followed by the classification.

© 2019 Optical Society of America

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