Feature Selection Parallel Technique for Remotely Sensed Imagery Classification
This work addresses a bottleneck in remote sensing image processing for researchers and practitioners, though it appears incremental as it builds on existing methods with a parallel implementation.
The paper tackles the performance issues of dependence-based feature selection methods for remotely sensed imagery classification by proposing a parallel approach, achieving promising results on hyperspectral and high spatial resolution images.
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.