Spectral PPCA transform and spatial wavelets using lifting technique for data compression of digital hyperspectral images

Wouter Verhoef

doi:10.1117/12.413914

19 January 2001 Spectral PPCA transform and spatial wavelets using lifting technique for data compression of digital hyperspectral images

Wouter Verhoef

Author Affiliations +

Proceedings Volume 4170, Image and Signal Processing for Remote Sensing VI; (2001) https://doi.org/10.1117/12.413914
Event: Europto Remote Sensing, 2000, Barcelona, Spain

Abstract

Lifting has been recognised as an effective numerical technique to realise linear transformations to digital data in integer-to-integer form, which guarantees perfect reversibility. When applied to decorrelate digital hyperspectral images in the spectral and spatial domains, lifting can be applied to accomplish lossless data compression. Spectral pairwise principal component analysis (PPCA) and spatial wavelet transforms have been combined to demonstrate data compression of digital hyperspectral images acquired by the AVIRIS instrument, and in both transforms lifting has been applied to realise an efficient algorithm, suitable for on-board implementation in a spacebome imaging spectrometer. The cascaded spectral PPCA algorithm produces a large number of noisy images, which subsequently are compressed using a general purpose Lempel-Ziv coder. The resulting signal images are spatially decorrelated using a wavelet transform, and an embedded zerotree encoder (EZT) is applied to achieve data compression for these. Uniform linear quantisation of the spectrally and spatially decorrelated data is applied to allow for quasi-lossless compression, in which case a higher compression ratio is obtained. The overall compression factors obtained for 16-bit AVIRIS data from two scenes vary from about two for lossless compression to four for quasi-lossless compression with an rms error of 2% of the input standard deviation.

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Wouter Verhoef "Spectral PPCA transform and spatial wavelets using lifting technique for data compression of digital hyperspectral images", Proc. SPIE 4170, Image and Signal Processing for Remote Sensing VI, (19 January 2001); https://doi.org/10.1117/12.413914

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