Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 15, Issue 9, Pages 1427-1431Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2018.2841400
Keywords
Deep learning; hyperspectral remote sensing; nonnegative sparse autoencoder (NNSAE); unmixing
Categories
Funding
- National Natural Science Foundation of China [61771496]
- National Key Research and Development Program of China [2017YFB0502900]
- Guangdong Provincial Natural Science Foundation [2016A030313254]
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As an unsupervised learning tool, autoencoder has been widely applied in many fields. In this letter, we propose a new robust unmixing algorithm that is based on stacked nonnegative sparse autoencoders (NNSAEs) for hyperspectral data with outliers and low signal-to-noise ratio. The proposed stacked autoencoders network contains two main steps. In the first step, a series of NNSAE is used to detect the outliers in the data. In the second step, a final autoencoder is performed for unmixing to achieve the endmember signatures and abundance fractions. By taking advantage from nonnegative sparse autoencoding, the proposed approach can well tackle problems with outliers and low noise-signal ratio. The effectiveness of the proposed method is evaluated on both synthetic and real hyperspectral data. In comparison with other unmixing methods, the proposed approach demonstrates competitive performance.
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