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

摘要

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 decorrealte 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 spaceborne 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 resultign 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 otained. 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 2percent of the input standard deviation.