MULTITASK LEARNING FOR SPATIAL-SPECTRAL HYPERSPECTRAL UNMIXING

This paper introduces a novel deep learning based method for blind hyperspectral unmixing. The technique utilizes multitask learning (MTL) through multiple parallel autoencoders to simultaneously unmix a neighborhood of pixels at a time. The sharing of hidden representations between autoencoders enables the method to take advantage of spatial correlations in the hyperspectral image (HSI). The method is evaluated using real HSI data and compared to three state-of-the-art techniques. The proposed method outperforms all the comparison methods in the experiments.