International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004
4. CONCLUSIONS AND DISCUSSIONS
The 3D wavelet is a multi-band wavelet analyse and it can
obtain edge and structure feature with higher accuracy, which is
very use for image compression to obtain high reconstruct
image quality. In this paper, the 3D wavelet transformation is
proposed for the edge reservation image compression. The
experiments of three typical multispectrum sensor obtain over
than 16 compression ratio. The reconstruct image quality can
meet the quassi-lossless requirement. It can be use for the image
database buiding and image transmitting. The further
improvements of the proposed method can be summarized as
followings:
(1) Edge extraction algorithm: to obtain high precision edge
and structure feature of the objects in the multispectrum image
(2) Quantification poly to wavelet coefficient
ACKNOWLEDGEMENT
The author would like to thank MODIS Research Center to
provide MODIS images.
REFERENCES
[2] A. K. Jain, Fundamentals of digital image processing,
Prentice-Hall International Editions, 1991.
[3] J. A. Saghri, et:al:, Practical Transform Coding of
Multispectral Imagery," IEEE Signal Processing Magazine, pp.
32-43 Nov. 1995.
ZHOU S T, XUAN J B. Restoration Technique Based on Image
Features. The Journal of Wuhan Technical University of
Surveying and Mapping, 1999.V01.24(3):230-234.
WU L N. Principle and Application of Data Compression.
Beijing: Publishing House of Electronic Industry, 1994.
ZHU X CH, HU D. Digital Image Communication. Beijing:
Publishing House of the People’s Posts and
Telecommunications, 1995.
HU Qingwu, Technique of Quasi-lossless Compression of
Multiple Spectrum Remote Sensing Images Based On Image
Restoration. SPIE: 4551-37,2001
Zhang Rong , Yan Qing , Liu Zhengkai . A Prediction Tree
based Lossless Compression Technique of Multispectral Image
Data [ J ] .Journal of Remote Sensing , 1998 , 2 (3) : 171-175.
Zhang Rong , Liu Zhengkai , Li Hougiang. Classification based
lossless compression of multispectral image [J ]. Journal of
Image and Graphics , 1998 , 3(2) : 106 110.
Hoffman R N , Johnson D W. Application of EOF' s to
multispectral imagery: data comp ression and no ise detection
for AV IR IS [J ]. IEEE Trans Geosci Remote Sensing, 1994,
32 (1): 25 34
Memon N D, Sayood K, N agliras S S. Lossless compression of
multispectral image data [ J ]. IEEE Trans Geosci Remote
Sensing, 1994, 32 (2) : 282 — 289
W ang J F, Zhang K, Tang S. Spectral and spatial decorrelation
of LandsatTM data fo r lo ssless compression [J ]. IEEE Trans
Geosci Remo te Sensing,1995, 33 (5) : 1277— 1285
Rao A K, Bhargava S. M ultispectral data comp ression using
60
bidirectional interband prediction[J ].
Remote Sensing, 1996, 34 (2)
Said A Pearlman W A. A n image multiresolution
representation for lossless and lossy comp ression [ J ]. IEEE
Trans Image Processing, 1996, 5 (9) : 1303— 1310
IEEE Trans Geosei
WU Jianhua, Image data compression of GSM satellite image
[J ]. Journal of Image and Graphics, 1999, 4 (1) : 56— 60
Dep
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