Given the limitation of traditional univariate analysis method in processing the multicollinearity of dam monitoring data, this paper reconstructs the multivariate response variables by introducing principal component analysis (PCA) method, explores the ways of determining principal components (PCs), and extracts a few PCs that have major influence on data variance. For steady observation series, a control field for the whole observation values has been established based upon PCA; for unsteady observation series that have significant tendency, a control field for the future observation values has been constructed according to PC statistical predication model. These methods have already been applied to an actual project and the results showed that data interpretation method with PCA can not only realize data reduction, lower data redundancy, and reduce noise and false alarm rate, but also be effective to data analysis, having a broad application prospect. dam safety monitoring, multivariate response variables, principal component analysis, data reduction Citation:Yu H, Wu Z R, Bao T F, et al. Multivariate analysis in dam monitoring data with PCA.

Dimensionality reduction (DR) is an important preprocessing step in hyperspectral image applications. In this paper, a superpixelwise kernel principal component analysis (SuperKPCA) method for DR that performs kernel principal component analysis (KPCA) on each homogeneous region is proposed to fully utilize the KPCA’s ability to acquire nonlinear features. Moreover, for the proposed method, the differences in the DR results obtained based on different fundamental images (the first principal components obtained by principal component analysis (PCA), KPCA, and minimum noise fraction (MNF)) are compared. Extensive experiments show that when 5, 10, 20, and 30 samples from each class are selected, for the Indian Pines, Pavia University, and Salinas datasets: (1) when the most suitable fundamental image is selected, the classification accuracy obtained by SuperKPCA can be increased by 0.06%–0.74%, 3.88%–4.37%, and 0.39%–4.85%, respectively, when compared with SuperPCA, which performs PCA on each homogeneous region; (2) the DR results obtained based on different first principal components are different and complementary. By fusing the multiscale classification results obtained based on different first principal components, the classification accuracy can be increased by 0.54%–2.68%, 0.12%–1.10%, and 0.01%–0.08%, respectively, when compared with the method based only on the most suitable fundamental image.

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