Indexed on: 31 Mar '17Published on: 31 Mar '17Published in: IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
In hyperspectral image analysis, the classification task has generally been addressed jointly with dimensionality reduction due to both the high correlation between the spectral features and the noise present in spectral bands which might significantly degrade classification performance. In supervised classification, limited training instances in proportion to the number of spectral features have negative impacts on the classification accuracy, which has known as Hughes effects or curse of dimensionality in the literature. In this paper, we focus on dimensionality reduction problem, and propose a novel feature-selection algorithm which is based on the method called High Dimensional Model Representation. The proposed algorithm is tested on some toy examples and hyperspectral datasets in comparison to conventional feature-selection algorithms in terms of classification accuracy, stability of the selected features and computational time. The results showed that the proposed approach provides both high classification accuracy and robust features with a satisfactory computational time.