Improving Wishart Classification of Polarimetric SAR Data Using the Hopfield Neural Network Optimization Approach



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Pajares Martinsanz, Gonzalo and López-Martínez, Carlos and Sánchez-Lladó, F. and Molina, Íñigo (2012) Improving Wishart Classification of Polarimetric SAR Data Using the Hopfield Neural Network Optimization Approach. Remote Sensing, 4 (11). pp. 3571-3595. ISSN 2072-4292

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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.

Item Type:Article
Uncontrolled Keywords:Hopfield neural networks; image classification; Polarimetric Synthetic Aperture Radar (PolSAR); Wishart classifier; optimization
Subjects:Sciences > Computer science > Artificial intelligence
ID Code:67654
Deposited On:06 Sep 2021 11:36
Last Modified:06 Sep 2021 11:43

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