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Title
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UnDIP : hyperspectral unmixing using deep image prior
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Author
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Abstract
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| In this paper, we introduce a deep learning-based technique for the linear hyperspectral unmixing problem. The proposed method contains two main steps. First, the endmembers are extracted using a geometric endmember extraction method, i.e., a simplex volume maximization in the subspace of the dataset. Then, the abundances are estimated using a deep image prior. The main motivation of this work is to boost the abundance estimation and to make the unmixing problem robust to noise. 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 proposed method is evaluated on simulated and three real remote sensing data for a range of SNR values (i.e., from 20 to 50 dB). The results show considerable improvements compared to state-of-the-art methods. The proposed method was implemented in Python (3.8) using PyTorch as the platform for the deep network and is available online: https://github.com/BehnoodRasti/UnDIP. |
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Language
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English
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Source (journal)
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IEEE transactions on geoscience and remote sensing / Institute of Electrical and Electronics Engineers. - New York, N.Y., 1980, currens
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Publication
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New York, N.Y.
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2022
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ISSN
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0196-2892
[print]
1558-0644
[online]
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DOI
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10.1109/TGRS.2021.3067802
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Volume/pages
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60
(2022)
, 15 p.
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Article Reference
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5504615
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ISI
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000730619400046
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Medium
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E-only publicatie
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Full text (Publisher's DOI)
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Full text (open access)
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Full text (publisher's version - intranet only)
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