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Class centroid alignment based domain adaptation for classification of remote sensing images

机译：基于类质心对齐的域自适应用于遥感图像分类

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摘要

A new unsupervised domain adaptation algorithm based on class centroid alignment (CCA) is proposed for classification of remote sensing images. The approach aims to align the class centroids of two domains by moving the target domain samples toward source domain, with the moving direction equaling to the difference of the associated class centroids between two domains. After moving, the data distributions become similar and the classifier trained in source domain can be used to predict the changed target domain data. Since there lacks labeled information in target domain, the class centroids and moving directions are estimated based on the predicted results. Moreover, better moving directions can be determined by preserving the local similarity in the changed target domain, resulted in neighborhood based CCA (NCCA) method. Experiments with Hyperion, AVIRIS, and NCALM hyperspectral images and Worldview-2 multispectral images demonstrated the effectiveness of applying CCA and NCCA in reality. (C) 2016 Elsevier B.V. All rights reserved.
机译：提出了一种基于类重心对齐（CCA）的无监督域自适应算法，用于遥感图像的分类。该方法旨在通过将目标域样本移向源域来对齐两个域的类质心，其移动方向等于两个域之间关联的类质心的差。移动之后，数据分布变得相似，并且可以使用在源域中训练的分类器来预测更改后的目标域数据。由于目标域中缺少标记信息，因此将基于预测结果来估计类质心和运动方向。此外，可以通过在更改后的目标域中保留局部相似性来确定更好的移动方向，从而得出基于邻域的CCA（NCCA）方法。 Hyperion，AVIRIS和NCALM高光谱图像以及Worldview-2多光谱图像的实验证明了在实际中应用CCA和NCCA的有效性。 （C）2016 Elsevier B.V.保留所有权利。

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