Ensemble of transfer component analysis for domain adaptation in hyperspectral remote sensing image classification
Ensemble of transfer component analysis for domain adaptation in hyperspectral remote sensing image classification
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DOI：
10.1109/igarss.2017.8128066
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发表时间：
2017-07
期刊：
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通讯作者：
Junshi Xia;N. Yokoya;Akira Iwasaki
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文献类型：
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作者：
Junshi Xia;N. Yokoya;Akira Iwasaki
在这项工作中，我们解决了无监督域迁移学习的问题，通过集成策略的背景下，多个高光谱图像之间的分类。域自适应的目的是使用源图像中的标记样本将标记分配给感兴趣的图像（目标图像）。该方法是基于旋转系综和传输分量分析（TCA）。在该方法中，源图像和目标图像的特征空间被划分为若干互不相交的特征子集。然后，在源域中的TCA技术诱导的特征被用作随机森林（RF）分类器的输入空间。最后，通过多数投票融合每个步骤获得的结果。我们比较所提出的方法，合奏的TCA（E-TCA），一个定期的RF和RF减少功能的TCA。在日本混交林采集的真实的高光谱图像上的实验显示了显着的跨图像分类性能。
In this work, we address the problem of unsupervised domain transfer learning via an ensemble strategy in the context of classification between multiple hyperspectral images. The objective of domain adaption is to assign the label to an image of interest (the target image) using the labeled samples in the source image. The proposed method is based on the rotation-based ensemble and transfer component analysis (TCA). In this method, the feature space in both source and target image is divided into several disjoint feature subsets. Then, the features induced by the TCA technique in the source domain are used as the input space to a random forest (RF) classifier. Finally, the results achieved by each step are fused by a majority vote. We compare the proposed method, ensemble of TCA (E-TCA), with a regular RF and an RF with the reduced features by the TCA. Experiments on the real hyperspectral image acquired over a Japanese mixed forest show remarkable cross-image classification performances.