Semi-supervised multiview embedding for hyperspectral data classification
In this paper, a method for semi-supervised multiview feature extraction based on the multiset regularized kernel canonical correlation analysis (kCCA) is proposed for the classification of hyperspectral images. The covariance matrix of this type of data is naturally composed of distinct blocks of spectral channels, which in turn compose the hypercube. To reduce the dimensionality of the data and extract discriminant features taking advantage of this particular structure, a multiview feature extraction method is applied prior to the classification. The proposed scheme exploits both the labels (as a distinct view on the data) and unlabeled pixels into the computation of cross-correlations and regularizations terms. First, we propose a technique to automatically obtain the segmentation of the spectral profile, based on the correlation between channels. Then, the multiset kernel canonical correlation analysis is applied to find a latent space which represents mutually correlated projected views and labels. Experiments on three real hyperspectral images with two linear classifiers and comparisons to state-of-the-art feature extraction methods show the benefits of this approach, which provides classification accuracies equal or superior to those obtained by training classifiers on the original input space but with only a fraction of the original data dimensionality.
classification feature extraction kernel canonical correlation analysis hyperspectral images semi-supervised learning multiview learning