Hyperspectral image compression based on DLWT and PCA
Qiuyan Shi, Xingsong Hou, Xueming Qian · 2015
Each band, which is the image of the same object on different frequency bands for hyperspectral image, has not only the correlation in space, but also a strong correlation between spectrum. The hyperspectral image compression algorithms need to consider how to make use of the correlation of both space and spectrum. In this paper, we first use principal component analysis (PCA) to remove the spectral correlation. Then a directional lifting wavelet transform(DLWT) is used to remove the spatial correlation. The experimental results show that the proposed image compression scheme achieves higher performances when compared with DWT based Consultative Committee for Space Data Systems(CCSDS).