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

Molina Sánchez, Iñigo; Pajares Martinsanz, Gonzalo; López-Martínez, Carlos y Sánchez-Lladó, Francisco Javier (2012). Improving Wishart Classification of Polarimetric SAR Data Using the Hopfield Neural Network Optimization Approach.. "Remote Sensing", v. 4 (n. 11); pp. 3571-3595. ISSN 2072-4292. https://doi.org/10.3390/rs4113571.

Descripción

Título: Improving Wishart Classification of Polarimetric SAR Data Using the Hopfield Neural Network Optimization Approach.
Autor/es:
  • Molina Sánchez, Iñigo
  • Pajares Martinsanz, Gonzalo
  • López-Martínez, Carlos
  • Sánchez-Lladó, Francisco Javier
Tipo de Documento: Artículo
Título de Revista/Publicación: Remote Sensing
Fecha: Noviembre 2012
Volumen: 4
Materias:
Palabras Clave Informales: Hopfield neural networks; image classification; Polarimetric Synthetic Aperture Radar (PolSAR); Wishart classifier; optimization.
Escuela: E.T.S.I. en Topografía, Geodesia y Cartografía (UPM)
Departamento: Ingeniería Cartográfica, Geodesia y Fotogrametría [hasta 2014]
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Más información

ID de Registro: 15434
Identificador DC: http://oa.upm.es/15434/
Identificador OAI: oai:oa.upm.es:15434
Identificador DOI: 10.3390/rs4113571
URL Oficial: http://www.mdpi.com/journal/remotesensing
Depositado por: Memoria Investigacion
Depositado el: 08 Oct 2013 15:10
Ultima Modificación: 21 Abr 2016 15:29
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