  Polarimetric synthetic aperture radar (PolSAR) image classification has been
investigated vigorously in various remote sensing applications. However, it is
still a challenging task nowadays. One significant barrier lies in the speckle
effect embedded in the PolSAR imaging process, which greatly degrades the
quality of the images and further complicates the classification. To this end,
we present a novel PolSAR image classification method, which removes speckle
noise via low-rank (LR) feature extraction and enforces smoothness priors via
Markov random field (MRF). Specifically, we employ the mixture of
Gaussian-based robust LR matrix factorization to simultaneously extract
discriminative features and remove complex noises. Then, a classification map
is obtained by applying convolutional neural network with data augmentation on
the extracted features, where local consistency is implicitly involved, and the
insufficient label issue is alleviated. Finally, we refine the classification
map by MRF to enforce contextual smoothness. We conduct experiments on two
benchmark PolSAR datasets. Experimental results indicate that the proposed
method achieves promising classification performance and preferable spatial
consistency.
