基于小波变换的高光谱图像压缩算法初步研究
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摘要
摘 要
高光谱图像是由成像光谱仪在不同光谱波段获得的序列图像，在二维遥感图
像的基础上又增加了光谱维的信息。这种高分辨率、连续谱段的图像具有极大的
数据量，对传输和存储带来了不便。为此，针对具体应用的数据压缩技术被广泛
采用，包括有损压缩、近无损压缩和无损压缩技术。
由于高光谱图像主要用于对地物进行分类、识别、监测等等，压缩过程应尽
量不丢失有用的信息。论文主要研究高光谱图像的近无损和无损压缩技术，侧重
于无损压缩算法。另外，为了便于图像的检索和浏览，应能够提供从有损到无损
的具有渐进传输性能的压缩码流。
算法的研究以小波变换为基础，利用提升方法构造整数可逆小波变换，并根
据高光谱图像自身的特点，选择适合的整型小波变换，结合等级树集合分裂
(SPIHT)算法和集合分裂嵌入式块编码(SPECK)算法，利用位平面编码的思想实
现波段图像的渐进传输，最终得到无损压缩的结果。此外，根据光谱波段之间的
相关性分析结果对若干波段进行分组，将各组波段图像组织成数据立方体的形式
进行三维小波变换，在去除图像空间相关性的同时，也去除了波段图像之间的相
关性，从而进一步降低图像的熵值。再运用 3-D SPIHT 算法对三维小波系数进行
编码，实现了无损压缩，相比直接使用二维方法压缩，压缩比有了进一步提高。
由于光谱维相关性很强，进行波段分组后，先在光谱方向进行小波变换，去除波
段图像之间的相关性，然后再单独对各个波段使用 SPIHT 或 SPECK 算法，也获
得了很好的无损压缩结果。
论文还初步探索了基于小波变换的视频编码算法用于光谱图像的压缩，取得
了初步的结果。
Hyperspectral image, an image sequence generated by spectrometer in many
narrow ranges of wavelength, possesses additional spectrum information
corresponding to traditional two dimensional remote sensing images. The character of
the high spatial resolution and high spectral resolution makes the hyperspectral image
occupy huge set of data, which results in heavy burden for data transmission and
storage. Therefore, some application-specific data compression techniques should be
applied, including lossy, near-lossless and lossless compression.
Hyperspectral imaging is a promising technique and mainly used for surface
detection and identification, target classification and status monitoring. The
compression scheme should not lose useful information in original data. In this paper,
we study related lossless and near-lossless compression for hyperspectral image.
Moreover, the compression scheme provides embedded bitstream from lossy to
lossless, which facilitates the image retrieving and browsing.
The proposed algorithms here are based on the wavelet transform. For lossless
compression, the lifting scheme constructs the reversible integer wavelet transform.
The choice of the wavelet type based on the analysis of the character of hyperspectral
image, then, SPIHT or SPECK algorithm will be applied for bitplane coding that can
realize the lossy to lossless progressive transmission. We also exploit the correlation
between adjacent bands, the result implies that group of bands can form rectangular
prism, thus the three dimensional transform can act on it, causing decreased
correlation both in spatial dimension and spectral dimension, finally 3-D SPIHT
algorithm sorts the wavelet coefficient along the path of 3-D trees. The result of
lossless algorithm has slight improvement than that of 2-D algorithm. The most
surprising way is not the 3-D algorithm, but the 2-D algorithm used for decorrelated
groups of bands, i.e. applying wavelet transform first on spectral direction, then 2-D
algorithm is used to explore the coefficients’ relation. The experiment gives the
stirring result for lossless compression.
Video coding methods based on wavelet transform are introduced in this paper for
hyperspectral image compressing, the result is acceptable for near lossless
compression but still have some other disadvantages such as highly computing
complexity. The study here is elementary and still has a long way to go.
高光谱图像是由成像光谱仪在不同光谱波段获得的序列图像，在二维遥感图
像的基础上又增加了光谱维的信息。这种高分辨率、连续谱段的图像具有极大的
数据量，对传输和存储带来了不便。为此，针对具体应用的数据压缩技术被广泛
采用，包括有损压缩、近无损压缩和无损压缩技术。
由于高光谱图像主要用于对地物进行分类、识别、监测等等，压缩过程应尽
量不丢失有用的信息。论文主要研究高光谱图像的近无损和无损压缩技术，侧重
于无损压缩算法。另外，为了便于图像的检索和浏览，应能够提供从有损到无损
的具有渐进传输性能的压缩码流。
算法的研究以小波变换为基础，利用提升方法构造整数可逆小波变换，并根
据高光谱图像自身的特点，选择适合的整型小波变换，结合等级树集合分裂
(SPIHT)算法和集合分裂嵌入式块编码(SPECK)算法，利用位平面编码的思想实
现波段图像的渐进传输，最终得到无损压缩的结果。此外，根据光谱波段之间的
相关性分析结果对若干波段进行分组，将各组波段图像组织成数据立方体的形式
进行三维小波变换，在去除图像空间相关性的同时，也去除了波段图像之间的相
关性，从而进一步降低图像的熵值。再运用 3-D SPIHT 算法对三维小波系数进行
编码，实现了无损压缩，相比直接使用二维方法压缩，压缩比有了进一步提高。
由于光谱维相关性很强，进行波段分组后，先在光谱方向进行小波变换，去除波
段图像之间的相关性，然后再单独对各个波段使用 SPIHT 或 SPECK 算法，也获
得了很好的无损压缩结果。
论文还初步探索了基于小波变换的视频编码算法用于光谱图像的压缩，取得
了初步的结果。
Hyperspectral image, an image sequence generated by spectrometer in many
narrow ranges of wavelength, possesses additional spectrum information
corresponding to traditional two dimensional remote sensing images. The character of
the high spatial resolution and high spectral resolution makes the hyperspectral image
occupy huge set of data, which results in heavy burden for data transmission and
storage. Therefore, some application-specific data compression techniques should be
applied, including lossy, near-lossless and lossless compression.
Hyperspectral imaging is a promising technique and mainly used for surface
detection and identification, target classification and status monitoring. The
compression scheme should not lose useful information in original data. In this paper,
we study related lossless and near-lossless compression for hyperspectral image.
Moreover, the compression scheme provides embedded bitstream from lossy to
lossless, which facilitates the image retrieving and browsing.
The proposed algorithms here are based on the wavelet transform. For lossless
compression, the lifting scheme constructs the reversible integer wavelet transform.
The choice of the wavelet type based on the analysis of the character of hyperspectral
image, then, SPIHT or SPECK algorithm will be applied for bitplane coding that can
realize the lossy to lossless progressive transmission. We also exploit the correlation
between adjacent bands, the result implies that group of bands can form rectangular
prism, thus the three dimensional transform can act on it, causing decreased
correlation both in spatial dimension and spectral dimension, finally 3-D SPIHT
algorithm sorts the wavelet coefficient along the path of 3-D trees. The result of
lossless algorithm has slight improvement than that of 2-D algorithm. The most
surprising way is not the 3-D algorithm, but the 2-D algorithm used for decorrelated
groups of bands, i.e. applying wavelet transform first on spectral direction, then 2-D
algorithm is used to explore the coefficients’ relation. The experiment gives the
stirring result for lossless compression.
Video coding methods based on wavelet transform are introduced in this paper for
hyperspectral image compressing, the result is acceptable for near lossless
compression but still have some other disadvantages such as highly computing
complexity. The study here is elementary and still has a long way to go.
引文
参考文献
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Compression Conf. 1993:351~360
11 M. Weinberger, G. Seroussi, G. Sapiro. The LOCO-I lossless image compression
algorithm: principles and standardization into JPEG-LS. HPL-98-193, Nov.1998.
12 Xiaolin Wu. Nasir Memon. Context-based, adaptive, lossless image coding. IEEE
Trans. on Communications. 1997,45(4):437~444
13 P. G. Howard, J. S. Vitter. Arithmetic Coding for Data Compression. Proceedings
of the IEEE. 1994,82(6): 857~865
14 Rice, R.F. Some practical universal noiseless coding techniques. Technical Report
79-22. Jet Propulsion Laboratory, California Institute of Technology, Pasadena,
CA. (1979)
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http://dynamo.ecn.purdue.edu/~bouman/publications/pdf/multispec.pdf
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21 Gregory K.Wallace, The JPEG still picture compression standard, IEEE
Transactions on Consumer Electronics. 1992,38(1)
22 A. Skodras, C. Christopoulos. T. Ebrahimi. The JPEG 2000 still image
compression standard. IEEE Trans. on Signal Processing. 2001,18(5):36~58
23 M. Rabbani, R. Joshi. An overview of the JPEG2000 still image compression
standard. Signal Processing: Image Communication 2002,17:3~48
24 Charilaos, et al. Efficient methods for encoding region of interest in the upcoming
JPEG2000 still image coding standard. IEEE Signal Processing Letters. 2000,7(9):
247~249
25 David Taubman. High Performance Scalable Image Compression with EBCOT.
IEEE Transactions on Image Proc, 2000, 9(7): 1158~1170
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Signal Processing, One Microsoft Way, Bld. 113/3374, Redmond, WA 98052:
16~17
30 J.S. Houchin, D.W. Singer. File format technology in JPEG 2000 enables flexible
use of still and motion sequences. Signal Processing: Image Communication.
2002,17: 131~144
31 ISO/IEC. A study of JPEG 2000 still image coding versus other standards.
JTC1/SC29/WG1 N1814, July 2000
32 孙家柄. 遥感原理与应用. 武汉大学出版社. 2003:83~88
33 David Landgrebe. Hyperspectral Image Data Analysis. IEEE Signal Processing
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34 刘恒殊等. 超光谱遥感图像特征分析. 光学精密工程. 2001,9(4):392-395
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1 朱述龙. 张占睦. 遥感图像获取与分析. 科学出版社. 2000: 1~5
2 王先华. 王乐意. 乔延利. 遥感中的高光谱技术及应用. 光电子技术与信息.
2001,14(6):7~13
3 浦瑞良. 宫鹏. 高光谱遥感及其应用. 高等教育出版社. 2000: 3~27
4 杨哲海. 韩建峰. 宫大鹏等. 高光谱遥感技术的发展与应用. 海洋测绘.
2003,23(6):55~58
5 崔廷伟. 马毅. 张杰. 航空高光谱遥感的发展与应用. 遥感技术与应用.
2003,18(2):118~121
6 刘 建 贵 . 成 像 光 谱 数 据 在 城 市 遥 感 中 的 应 用 研 究 . 遥 感 学 报 .
2000,4(3):224~227
7 刘银年. 薛永祺. 王建宇等. 实用型模块化成像光谱仪. 红外与毫米波学报.
2002,21(1): 9~13
8 沈兰荪. 图象编码与异步传输. 人民邮电出版社. 1998
9 沈世镒. 陈鲁生. 信息论与编码理论. 科学出版社. 2002
10 P. G. Howard, J. S. Vitter. Fast and efficient lossless image compression. Data
Compression Conf. 1993:351~360
11 M. Weinberger, G. Seroussi, G. Sapiro. The LOCO-I lossless image compression
algorithm: principles and standardization into JPEG-LS. HPL-98-193, Nov.1998.
12 Xiaolin Wu. Nasir Memon. Context-based, adaptive, lossless image coding. IEEE
Trans. on Communications. 1997,45(4):437~444
13 P. G. Howard, J. S. Vitter. Arithmetic Coding for Data Compression. Proceedings
of the IEEE. 1994,82(6): 857~865
14 Rice, R.F. Some practical universal noiseless coding techniques. Technical Report
79-22. Jet Propulsion Laboratory, California Institute of Technology, Pasadena,
CA. (1979)
15 沈兰荪. 魏海. 图像的无损压缩研究. 数据采集与处理. 1999,14(4):485~491
16 D. Tretter, N. Memon, C. A. Bouman. Multispectral Image Coding.
http://dynamo.ecn.purdue.edu/~bouman/publications/pdf/multispec.pdf
17 ISO/IEC. JBIG IS. 11544 (ITU-T T.82). 1992
18 ISO/IEC. JPEG IS. 10918-1. 1993
19 ISO/IEC. JPEG-LS Part 2. FCD14495-2, April 2000
20 ISO/IEC. JPEG2000 Part I Final Draft International Standard. FDIS 15444-1.
67
北京工业大学工学硕士学位论文
JEC1/SC29/WG1 N1855. August 18. 2000
21 Gregory K.Wallace, The JPEG still picture compression standard, IEEE
Transactions on Consumer Electronics. 1992,38(1)
22 A. Skodras, C. Christopoulos. T. Ebrahimi. The JPEG 2000 still image
compression standard. IEEE Trans. on Signal Processing. 2001,18(5):36~58
23 M. Rabbani, R. Joshi. An overview of the JPEG2000 still image compression
standard. Signal Processing: Image Communication 2002,17:3~48
24 Charilaos, et al. Efficient methods for encoding region of interest in the upcoming
JPEG2000 still image coding standard. IEEE Signal Processing Letters. 2000,7(9):
247~249
25 David Taubman. High Performance Scalable Image Compression with EBCOT.
IEEE Transactions on Image Proc, 2000, 9(7): 1158~1170
26 David Taubman, Erik Ordentlich, et al. Embedded block coding in JPEG2000.
IEEE International Conference on Image Processing. 2000,2:33~36
27 Shapiro J M. Embedded image coding using zerotree of wavelet coefficients.
IEEE Trans. on Signal Processing. 1993,41(12):3445~3462
28 A. Said, W. Pearlman. A New, Fast and Efficient Image Codec Based on Set
Partitioning in Hierarchical Trees，IEEE Trans. on Circuit and System for Video
Technology. 1996,6(3):234~250
29 Jin Li. Image Compression--the Mechanics of the JPEG 2000. Microsoft Research,
Signal Processing, One Microsoft Way, Bld. 113/3374, Redmond, WA 98052:
16~17
30 J.S. Houchin, D.W. Singer. File format technology in JPEG 2000 enables flexible
use of still and motion sequences. Signal Processing: Image Communication.
2002,17: 131~144
31 ISO/IEC. A study of JPEG 2000 still image coding versus other standards.
JTC1/SC29/WG1 N1814, July 2000
32 孙家柄. 遥感原理与应用. 武汉大学出版社. 2003:83~88
33 David Landgrebe. Hyperspectral Image Data Analysis. IEEE Signal Processing
Magazine. 2002:17~28
34 刘恒殊等. 超光谱遥感图像特征分析. 光学精密工程. 2001,9(4):392-395
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