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EnglishIn this paper, we propose a deep auto-encoder network for the unmixing for hyperspectral data with outliers and low signal to noise ratio. The proposed deep auto-encoder net-work composes of two parts. The first part of the network adopts stacked non-negative sparse auto-encoder to learn the spectral signatures such that to generate a good initialization for the network. In the second part of the network, a varia-tional auto-encoder is employed to perform unmixing, aiming at the endmember signatures and abundance fractions. The effectiveness of the proposed method is verified by using a synthetic data set. In our comparison with other state-of-the- A rt unmixing methods, the proposed approach demonstrates highly competitive performance.
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| Titolo: | Deep auto-encoder network for hyperspectral image unmixing |
| Autori: | |
| Data di pubblicazione: | 2018 |
| Serie: | |
| Abstract: | In this paper, we propose a deep auto-encoder network for the unmixing for hyperspectral data with outliers and low signal to noise ratio. The proposed deep auto-encoder net-work composes of two parts. The first part of the network adopts stacked non-negative sparse auto-encoder to learn the spectral signatures such that to generate a good initialization for the network. In the second part of the network, a varia-tional auto-encoder is employed to perform unmixing, aiming at the endmember signatures and abundance fractions. The effectiveness of the proposed method is verified by using a synthetic data set. In our comparison with other state-of-the- A rt unmixing methods, the proposed approach demonstrates highly competitive performance. |
| Handle: | http://hdl.handle.net/11571/1347104 |
| ISBN: | 978-1-5386-7150-4 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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