A Method of Polarimetric Synthetic Aperture Radar Image Classification Based on Sparse Representation
Abstract: Sparse
representation-based techniques have shown great potential for pattern
recognition problems. Therefore, on the basis of the sparse characteristics of
the features for PolSAR image classification, a supervised PolSAR image
classification method based on sparse representation is proposed in this paper.
It works by projecting the feature vector of the pending pixel onto a subset of
training vectors from dictionary and then obtains the corresponding optimal
coefficients as well as theresidual error with respect to each atom. Then, the
residual errors of the pending pixel with respect to each atom are evaluated
and considered as the criteria for classification, namely, the ultimate class
can be obtained according to the atoms with the least residual error. The
verified experiment is implemented using Danish EMISAR L-band fully PolSAR data
of Foulum Area (DK) to validate the performance of theproposed classification
method.The preliminary experimental results confirm that the proposed method outputs
an excellent result and moreover the classification process is simpler and less
time consuming.
Author: Hongfu Wang, Xiaorong
Xue
Journal Code: jptkomputergg160053
