Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 57, Issue 4, Pages 2305-2323Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2018.2872850
Keywords
Alignment; classification; domain adaptation; remote sensing
Categories
Funding
- National Natural Science Foundation of China [61771437, 61102104, 91442201]
- Open Research Fund of Key Laboratory of Spectral Imaging Technology, Chinese Academy of Sciences [LSIT201702D]
- Purdue Laboratory for Applications of Remote Sensing
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A new domain adaptation algorithm based on the class centroid and covariance alignment (CCCA) is proposed for classification of remote sensing images. This approach exploits both the first- and second-order statistics to describe the data distribution and aligns the data distribution between domains on a per-class basis. Since the predicted labels of target data are used to estimate the two statistics, we applied overall centroid alignment (OCA) as a coarse domain adaptation strategy to improve the estimation accuracy. In addition, the OCA coarse adaptation in conjunction with CCCA refined adaptation can also benefit by incorporation of spatial information, resulting in a Spa_OCA_CCCA approach. The proposed approach is easy to implement, and only one parameter is required in the spatial filtering step. It does not require labeled information in the target domain and can achieve labor-free classification. The experimental results using Hyperion, National Center for Airborne Laser Mapping, and Worldview-2 remote sensing images demonstrated the effectiveness of the proposed approach.
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