@inproceedings{618611c59673459fae179531c13924c9,
title = "Hyperspectral image compression based on DLWT and PCA",
abstract = "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).",
keywords = "CCSDS, Compression, DLWT, Hyperspectral image, PCA",
author = "Qiuyan Shi and Xingsong Hou and Xueming Qian",
note = "Publisher Copyright: {\textcopyright} 2015 ACM.; 7th International Conference on Internet Multimedia Computing and Service, ICIMCS 2015 ; Conference date: 19-08-2015 Through 21-08-2015",
year = "2015",
month = aug,
day = "19",
doi = "10.1145/2808492.2808525",
language = "英语",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "347--351",
editor = "Ramesh Jain and Shuqiang Jiang and John Smith and Jitao Sang and Guohui Li and Tianzhu Zhang and Shuhui Wang",
booktitle = "ICIMCS 2015 - Proceedings of the 7th International Conference on Internet Multimedia Computing and Service",
}