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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). © 2015 ACM.
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ACM International Conference Proceeding Series
ISSN: 9781450335287
Year: 2015
Publish Date: August 19, 2015
Volume: 2015-August
Page: 347-351
Language: English
Cited Count:
WoS CC Cited Count: 0
SCOPUS Cited Count: 6
ESI Highly Cited Papers on the List: 0 Unfold All
WanFang Cited Count:
Chinese Cited Count:
30 Days PV: 4
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