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Compression of Hyperspectral Images

Cheng, Kai-Jen
http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1385471290

Abstract Details

2013, Doctor of Philosophy (PhD), Ohio University, Electrical Engineering & Computer Science (Engineering and Technology).
Hyperspectral images contain a wealth of spectral data, and occupy hundreds of megabytes, which makes the transmission to remote reception sites more challenging and difficult. Thus, compression schemes oriented to the task of remote transmission are becoming increasingly of interest in hyperspectral applications. In this dissertation, we develop a transform-based coding for high-dimensional hyperspectral images. We study Shapiro's EZW algorithm according to multiple modifications and the results show that the asymmetric transform and tree design have best performance in compression; in addition, the data dependent algorithm results in more compact outputs. We also present the performance of hybrid transforms, including the discrete wavelet transform and Karhunen-Loeve transform, and the new asymmetric spatial-spectral tree structure. The results also show that the hybrid transform results in optimal energy distribution in spatial and spectral dimensions; moreover, the long spatial-spectral tree makes compression more efficient. We propose a Binary Embedded Zerotrees Wavelet (BEZW) algorithm for hyperspectral images. The zerotree quantization strategy of the BEZW is designed for the hybrid transformed images and the dual tree structures are defined in order to predict the insignificant pixels. For lossy hyperspectral image compression, the suitable quality criteria have to consider spectral information and reflect spectral loss. In this research we list spectral distortion measurements, examined distortion on lossy compression, and compare their abilities to accurately characterize compression fidelity in end user applications, such as unsupervised classification of image pixels. Finally, we cover the lossy and lossless results of the BEZW algorithm on AVIRIS datasets and comparisons of the conventional transform-based coders and the best predictive coders in terms of the complexity and distortion criteria. The BEZW algorithm is competitive with the best predictive algorithms and also is an efficient computational method which is comparable to transform-based algorithms.
Jeffrey Dill (Advisor)
Chris Bartone (Committee Member)
Bryan Riley (Committee Member)
Jundong Liu (Committee Member)
Martin Mohlenkamp (Committee Member)
Sergio Lopez- Permouth (Committee Member)
163 p.
Computer Science; Electrical Engineering
Transform Based Coder; Wavelet Transform; Hyperspectral Image; Embedded Zerotree Wavelet; Karhunen Loeve Transform;

Recommended Citations

Citations

  • Cheng, K.-J. (2013). Compression of Hyperspectral Images [Doctoral dissertation, Ohio University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1385471290

    APA Style (7th edition)

  • Cheng, Kai-Jen. Compression of Hyperspectral Images. 2013. Ohio University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1385471290.

    MLA Style (8th edition)

  • Cheng, Kai-Jen. "Compression of Hyperspectral Images." Doctoral dissertation, Ohio University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1385471290

    Chicago Manual of Style (17th edition)

ohiou1385471290
2,090
© 2013, all rights reserved.
This open access ETD is published by Ohio University and OhioLINK.