Best rank-r tensor selection using Genetic Algorithm for better noise reduction and compression of Hyperspectral images

@article{Karami2010BestRT,
  title={Best rank-r tensor selection using Genetic Algorithm for better noise reduction and compression of Hyperspectral images},
  author={Azam Karami and Mehran Yazdi and Alireza Zolghadre Asli},
  journal={2010 Fifth International Conference on Digital Information Management (ICDIM)},
  year={2010},
  pages={169-173}
}
Hyperspectral images exhibit significant spectral correlation, whose exploitation is crucial for compression. In this paper, an efficient method for jointly compression and noise reduction of Hyperspectral images based on the Hierarchical Nonnegative Tucker Decomposition (HNTD) is presented. This algorithm not only exploits redundancies between bands but… CONTINUE READING