Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 16, Issue 11, Pages 1781-1785Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2019.2909543
Keywords
Manifolds; Hyperspectral imaging; Imaging; Support vector machines; Optimization; Classification; extreme learning machine (ELM); heterogeneous domain adaptation (HDA); manifold regularization; remote sensing
Categories
Funding
- Hyperspectral Image Analysis Group
- NSF
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An extreme learning machine (ELM)-based heterogeneous domain adaptation (HDA) algorithm is proposed for the classification of remote sensing images. In the adaptive ELM network, one hidden layer is used for the source data to provide the random features, whereas two hidden layers are set for target data to produce the random features as well as a transformation matrix. DA is achieved by constraining both the source data and the transformed target data to share the same output weights. Moreover, manifold regularization is adopted to preserve the local geometry of unlabeled target data. The proposed ELM-based HDA (EHDA) method is applied to cross-domain classification of remote sensing images, and the experimental results using multisensor remote sensing images demonstrate the effectiveness of the proposed approach.
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