Bayesian classification of hyperspectral images using spatially-varying Gaussian mixture model

KAYABOL K., Kutluk S.

DIGITAL SIGNAL PROCESSING, cilt.59, ss.106-114, 2016 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 59
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1016/j.dsp.2016.08.010
  • Dergi Adı: DIGITAL SIGNAL PROCESSING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.106-114
  • İstanbul Üniversitesi Adresli: Evet

Özet

We propose a spatially-varying Gaussian mixture model for joint spectral and spatial classification of hyperspectral images. The model provides a robust estimation framework for small sample size training sets. Defining prior distributions for the mean vector and the covariance matrix enables us to regularize the parameter estimation problem. More specifically, we can obtain invertible positive definite covariance matrices by the help of this regularization. Moreover, the proposed model also takes into account the spatial alignments of the pixels by using spatially-varying mixture proportions. The spatially-varying mixture model is based on spatial multinomial logistic regression. The classification results obtained on Indian Pines, Pavia Centre, Pavia University, and Salinas data sets show that the proposed methods perform better especially for small-sized training sets compared to the state-of-the-art classifiers. (C) 2016 Elsevier Inc. All rights reserved.