Compression of Hyperspectral Images
Kai-Jen Cheng · OhioLink ETD Center (Ohio Library and Information Network) · 2013
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-Loève 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