Behnood Rasti, Bikram Koirala, Paul Scheunders, and Pedram Ghamisi

In this paper, we introduce ‘Unmixing Deep Image Prior’ (UnDIP), a deep learning-based technique for the linear hyperspectral unmixing problem. The proposed method contains two steps. First, the endmembers are extracted using a geometric endmember extraction method, i.e. a simplex volume maximization in a subspace of the dataset. Then, the abundances are estimated using a deep image prior. The proposed deep image prior uses a convolutional neural network to estimate the fractional abundances, relying on the extracted endmembers and the observed hyperspectral dataset. The results show considerable improvements compared to state-of-the-art methods.

2021 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 3829-3832, 2021-07-11.

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IARAI Authors
Dr. Pedram Ghamisi
Research
Earth Observation
Keywords
Convolutional Neural Networks, Deep Learning, Hyperspectral Unmixing, Remote Sensing

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