Advanced Gaussian MRF rotation-invariant texture features for classification of remote sensing imagery

Huawu Deng Huawu Deng,D.A Clausi

doi:10.1109/cvpr.2003.1211533

Advanced Gaussian MRF rotation-invariant texture features for classification of remote sensing imagery

Huawu Deng Huawu Deng, D.A Clausi

https://doi.org/10.1109/cvpr.2003.1211533

Copy DOI

Publication Date: Jun 18, 2003

Citations: 17

Affiliation: University of Waterloo

#Rotation-invariant Features #Markov Random Field + Show 8 more

Abstract
Full-Text PDF
Similar Papers

Abstract

The features based on Markov random field (MRF) models are usually sensitive to the rotation of image textures. The paper develops an anisotropic circular Gaussian MRF (ACGMRF) model for modeling rotated image textures and retrieving rotation-invariant texture features. To overcome the singularity problem of the least squares estimate (LSE) method, an approximate least squares estimate (ALSE) method is proposed to estimate the parameters of ACGMRF model. The rotation-invariant features can be obtained from the parameters of the ACGMRF model by the one-dimensional (1D) discrete Fourier transform (DFT). Significantly improved accuracy can be achieved by applying the rotation-invariant features to classify SAR (synthetic aperture radar) sea ice and Brodatz imagery.

Full Text