Compression of hyperspectral images with enhanced discriminant features

Abstract

We propose compression algorithms for hyperspectral images with enhanced discriminant features. As the dimension of remotely sensed images increases, the need for efficient compression algorithms for hyperspectral images also increases. However, when hyperspectral images are compressed with conventional image compression algorithms, which have been developed to minimize mean squared errors, discriminant features of the original data may be lost during the compression process. In this paper, we propose to apply preprocessing prior to compression in order to preserve such discriminant information. In particular, we enhance discriminant features before a compression algorithm is applied. Experiments show that the proposed method provides improved classification accuracies than the existing compression algorithms.