Hyperspectral image compression with the 3D dual-tree wavelet transform

The complex dual-tree discrete wavelet transform is explored for the coding of hyperspectral imagery using a coder that has previously demonstrated efficient video-coding performance. A noise-shaping process increases the sparsity of the redundant transform-coefficient set, resulting in a high degree of regional coherency within the coefficient subbands. This coherency, as well as correlation across subbands, is exploited by a coding algorithm that performs set-partitioning using k-d trees. Prior experiments have shown that the proposed set-partitioning algorithm outperforms state-of-the-art JPEG2000 when coding video. However, experimental results indicate that the same coder fails to show similar gains when coding hyperspectral data, suggesting that hyperspectral data does not have properties that can be exploited by the increased directionality of the dual-tree transform.