  Remote sensing research focusing on feature selection has long attracted the
attention of the remote sensing community because feature selection is a
prerequisite for image processing and various applications. Different feature
selection methods have been proposed to improve the classification accuracy.
They vary from basic search techniques to clonal selections, and various
optimal criteria have been investigated. Recently, methods using
dependence-based measures have attracted much attention due to their ability to
deal with very high dimensional datasets. However, these methods are based on
Cramers V test, which has performance issues with large datasets. In this
paper, we propose a parallel approach to improve their performance. We evaluate
our approach on hyper-spectral and high spatial resolution images and compare
it to the proposed methods with a centralized version as preliminary results.
The results are very promising.
