Journal
2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019)
Volume -, Issue -, Pages 3305-3308Publisher
IEEE
DOI: 10.1109/igarss.2019.8898437
Keywords
Domain adaptation; extreme learning machine; hyperspectral data
Categories
Funding
- National Natural Science Foundations of China [61771437, 61102104, 91442201]
- Open Research Fund of Key Laboratory of Spectral Imaging Technology, Chinese Academy of Sciences [LSIT201 702D]
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Cross domain extreme learning machine (CDELM) is an unsupervised domain adaptation algorithm. It achieves domain adaptation by minimizing the classification loss on source labeled data and utilizing the maximum mean discrepancy strategy. We apply this algorithm for classification of hyperspectral images, and improve it by introducing a transformation on source features, so that the target data and transformed source data can better share the same output weights in ELM network. The enhanced CDELM is denoted as ECDELM in this paper. The experimental results using Hyperion multi-temporal remote sensing images demonstrated the effectiveness of the improvement.
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