Abstract:
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This paper proposes the optimization relaxation approach based on the analogue
Hopfield Neural Network (HNN) for cluster refinement of pre-classified Polarimetric
Synthetic Aperture Radar (PolSAR) image data. We consider the initial classification
provided by the maximum-likelihood classifier based on the complex Wishart distribution,
which is then supplied to the HNN optimization approach. The goal is to improve the
classification results obtained by the Wishart approach. The classification improvement is
verified by computing a cluster separability coefficient and a measure of homogeneity
within the clusters. During the HNN optimization process, for each iteration and for each
pixel, two consistency coefficients are computed, taking into account two types of relations
between the pixel under consideration and its corresponding neighbors. Based on these
coefficients and on the information coming from the pixel itself, the pixel under study is
re-classified. Different experiments are carried out to verify that the proposed approach
outperforms other strategies, achieving the best results in terms of separability and a
trade-off with the homogeneity preserving relevant structures in the image. The performance
is also measured in terms of computational central processing unit (CPU) times. |