For polarimetric synthetic aperture radar (PolSAR) image
classification, each pixel can be represented by multiple fea-
tures from different perspectives, such as polarimetric feature
(PF), texture feature (TF) and color feature (CF). Both multi-
view canonical correlation analysis (MCCA) and multi-view
spectral embedding (MSE) are two unsupervised multi-view
subspace learning methods which search for different pro-
jection matrices for different features to combine multiple
features in a common low-dimensional feature space. How-
ever, MCCA emphasizes the correlation of multiple features
and MSE learns the complementarity of multiple features.
To deeply learn the relation of multiple features, we incor-
porate MCCA with MSE based on the label information and
a symmetric version of revised Wishart (SRW) distance for
supervised PolSAR image feature extraction. Experimental
results confirm that the proposed method can improve the
classification performance.