Principal component analysis with pre-emphasis for compression of hyperspectral imagery

In this paper, we propose to use the principal component analysis for the compression of hyperspectral images. When hyperspectral images are compressed using conventional image compression algorithms, discriminant features of original data may be lost during compression process. In order to preserve such discriminant information, we first apply a linear feature extraction method to the original data. Then, we emphasize discriminant features and use the principal component analysis in order to compress the images whose discriminant features are enhanced. Experiments show that the proposed method provides improved classification accuracies than existing compression algorithms.