Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 61, Issue -, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2023.3313933
Keywords
Active learning; graph learning; hyperspectral unmixing (HSU); semisupervised learning
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In this article, two graph-based semisupervised unmixing methods are proposed, which significantly improve blind unmixing performance by using a small number of training pixels and graph-based learning methods.
Hyperspectral unmixing (HSU) is an effective tool to ascertain the material composition of each pixel in a hyperspectral image with typically hundreds of spectral channels. In this article, we propose two graph-based semisupervised unmixing methods. The first one directly applies graph learning to the unmixing problem, while the second one solves an optimization problem that combines the linear unmixing model and a graph-based regularization term. Following a semisupervised framework, our methods require a very small number of training pixels that can be selected by a graph-based active learning method. We assume to obtain the ground-truth information at these selected pixels, which can be either the exact (EXT) abundance value or the one-hot (OH) pseudo-label. In practice, the latter is much easier to obtain, which can be achieved by minimally involving a human in the loop. Compared with other popular blind unmixing methods, our methods significantly improve performance with minimal supervision. Specifically, the experiments demonstrate that the proposed methods improve the state-of-the-art blind unmixing approaches by 50% or more using only 0.4% of training pixels.
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