Stacked Autoencoder Based Feature Extraction and Superpixel Generation
  for Multifrequency PolSAR Image Classification
Tushar Gadhiya Sumanth Tangirala Anil K. Roy
In this paper we are proposing classification algorithm for multifrequency
Polarimetric Synthetic Aperture Radar (PolSAR) image. Using PolSAR
decomposition algorithms 33 features are extracted from each frequency band of
the given image. Then, a two-layer autoencoder is used to reduce the
dimensionality of input feature vector while retaining useful features of the
input. This reduced dimensional feature vector is then applied to generate
superpixels using simple linear iterative clustering (SLIC) algorithm. Next, a
robust feature representation is constructed using both pixel as well as
superpixel information. Finally, softmax classifier is used to perform
classification task. The advantage of using superpixels is that it preserves
spatial information between neighbouring PolSAR pixels and therefore minimises
the effect of speckle noise during classification. Experiments have been
conducted on Flevoland dataset and the proposed method was found to be superior
to other methods available in the literature.