Speech and Vision Lab

  • Increase font size
  • Default font size
  • Decrease font size
Home Publications Advanced Publication Search
Multispectral Image Classification Using Gabor Filters and Stochastic Relaxation Neural Network
Research Area: Uncategorized Year: 1997
Type of Publication: Article Keywords: Multispectral image classification, Texture-based analysis, Constraint satisfaction neural networks, Gabor, filters, Stochastic relaxation strategy, Remote sensing, Synthetic aperture radar images.
Authors: P.P.Raghu, B.Yegnanarayana  
In this article, we propose a supervised classification scheme for multispectral image data based on the spectral as well as textural features. A filter bank consisting of Gabor wavelets is used to extract the features from the multispectral imagery. The classification model consists of three random processes, namely, feature formation, partition and label competition. The feature formation process models the multispectral texture features from the Gabor filter bank as a multivariate Gaussian distribution. The partition process and the label competition process represent a set of label constraints. These constraints are represented on a Hopfield neural network model, and a stochastic relaxation strategy is used to evolve a global minimum energy state of the network, corresponding to the maximum a posteriori (MAP) probability. The performance of the scheme is demonstrated on a variety of multispectral multipolar images obtained from SIR-C/X-SAR. © 1997 Elsevier Science Ltd. All Rights Reserved.
Digital version