Translator Disclaimer
14 October 2004 Predictive partitioned vector quantization for hyperspectral sounder data compression
Author Affiliations +
The compression of three-dimensional hyperspectral sounder data is a challenging task given its unprecedented size and nature. Vector quantization (VQ) is explored for the compression of this hyperspectral sounder data. The high dimensional vectors are partitioned into subvectors to reduce codebook search and storage complexity in coding of the data. The partitions are made by use of statistical properties of the sounder data in the spectral dimension. Moreover, the data is decorrelated at first to make it better suited for vector quantization. Due to the data characteristics, the iterative codebook generation procedure converges much faster and also leads to a better reconstruction of the sounder data. For lossless compression of the hyperspectral sounder data, the residual error and the quantization indices are entropy coded. The independent vector quantizers for different partitions make this scheme practical for compression of the large volume 3D hyperspectral sounder data.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bormin Huang, Alok Ahuja, Hung-Lung Allen Huang, Timothy J. Schmit, and Roger W. Heymann "Predictive partitioned vector quantization for hyperspectral sounder data compression", Proc. SPIE 5548, Atmospheric and Environmental Remote Sensing Data Processing and Utilization: an End-to-End System Perspective, (14 October 2004);

Back to Top