Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 58, Issue 11, Pages 7920-7930Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2020.2985072
Keywords
Data shift; distribution alignment; domain adaptation (DA); remote sensing image scene classification
Categories
Funding
- National Key Research and Development Program of China [2017YFC1405605]
- National Natural Science Foundation of China [41804118]
- Natural Science Foundation of Tianjin [19JCZDJC40000]
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Remote sensing image scene classification refers to assigning semantic labels according to the content of the remote sensing scenes. Most machine learning-based scene classification methods assume that training and testing data share the same distributions. However, in real application scenarios, this assumption is difficult to guarantee. Domain adaptation (DA) is a promising approach to address this problem by aligning the feature distribution of training and testing data. Inspired by the idea DA, in this article, we propose a correlation subspace dynamic distribution alignment (CS-DDA) method for remote sensing image scene classification. Aiming at the characteristics of remote sensing scenes, we introduce two strategies to balance the effects of source and target domains: subspace correlation maximization (SCM) and dynamic statistical distribution alignment (DSDA). On the one hand, SCM tries to avoid mapping source domain data into irrelevant subspace to preserve the representation information of the source domain. On the other hand, DSDA is proposed to reduce the data distribution discrepancy between aligned source and target domains. Specifically, DSDA is a dynamic adjustment process where an adaptive factor is learned to balance the interclass and intraclass distribution between domains. Moreover, we integrate SCM and DSDA into a uniform optimization framework, and the optimal solution can be converted to the generalized eigendecomposition problem by derivation. The experimental results indicate that the proposed method can generate better results when compared with other feature distribution alignment methods.
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