Lossless compression of 3D hyperspectral sounder data using the wavelet and Burrows-Wheeler transforms

Shih-Chieh Wei; Bormin Huang

doi:10.1117/12.560527

14 October 2004 Lossless compression of 3D hyperspectral sounder data using the wavelet and Burrows-Wheeler transforms

Shih-Chieh Wei, Bormin Huang

Proceedings Volume 5548, Atmospheric and Environmental Remote Sensing Data Processing and Utilization: an End-to-End System Perspective; (2004) https://doi.org/10.1117/12.560527
Event: Optical Science and Technology, the SPIE 49th Annual Meeting, 2004, Denver, Colorado, United States

Abstract

Hyperspectral sounder data is used for retrieval of useful geophysical parameters which promise better weather prediction. It features two characteristics. First it is huge in size with 2D spatial coverage and high spectral resolution in the infrared region. Second it allows low tolerance of noise and error in retrieving the geophysical parameters where a mathematically ill-posed problem is involved. Therefore compression is better to be lossless or near lossless for data transfer and archive. Meanwhile medical data from X-ray computerized tomography (CT) or magnetic resonance imaging (MRI) techniques also possesses similar characteristics. It provides motivation to apply lossless compression schemes for medical data to the hyperspectral sounder data. In this paper, we explore the use of a wavelet-based lossless data compression scheme for the 3D hyperspectral data which uses in sequence a forward difference scheme, an integer wavelet transform, a Burrows-Wheeler transform and an arithmetic coder. Compared to previous work, our approach is shown to outperform the CALIC and 3D EZW schemes.

Citation Download Citation

Shih-Chieh Wei and Bormin Huang "Lossless compression of 3D hyperspectral sounder data using the wavelet and Burrows-Wheeler transforms", Proc. SPIE 5548, Atmospheric and Environmental Remote Sensing Data Processing and Utilization: an End-to-End System Perspective, (14 October 2004); https://doi.org/10.1117/12.560527

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