ML Anthology

Advanced Gaussian MRF Rotation-Invariant Texture Features for Classification of Remote Sensing Imagery

Huawu Deng, David A. Clausi
CVPR 2003 pp. 685-690
doi:10.1109/CVPR.2003.1211533 /cvpr/2003/deng2003cvpr-advanced/

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.

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Cite

Text

Deng and Clausi. "Advanced Gaussian MRF Rotation-Invariant Texture Features for Classification of Remote Sensing Imagery." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2003. doi:10.1109/CVPR.2003.1211533

Markdown

[Deng and Clausi. "Advanced Gaussian MRF Rotation-Invariant Texture Features for Classification of Remote Sensing Imagery." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2003.](https://mlanthology.org/cvpr/2003/deng2003cvpr-advanced/) doi:10.1109/CVPR.2003.1211533

BibTeX

@inproceedings{deng2003cvpr-advanced,
  title     = {{Advanced Gaussian MRF Rotation-Invariant Texture Features for Classification of Remote Sensing Imagery}},
  author    = {Deng, Huawu and Clausi, David A.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2003},
  pages     = {685-690},
  doi       = {10.1109/CVPR.2003.1211533},
  url       = {https://mlanthology.org/cvpr/2003/deng2003cvpr-advanced/}
}