Software Implementation of CCSDS Recommended Hyperspectral Lossless Image Compression
Автор: Dharam Shah, Kuhelika Bera, Sanjay Joshi
Журнал: International Journal of Image, Graphics and Signal Processing(IJIGSP) @ijigsp
Статья в выпуске: 4 vol.7, 2015 года.
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HyperSpectral Imagers (HySI) are used in the spacecraft or aircrafts to get minute characteristics of target element through capturing image in a large number of narrow and contiguous bands. HySI data represented as data cube with two dimensions representing spatial distribution and third dimension providing band information is huge in volume and challenging task to handle. Hence onboard compression becomes a necessary for optimal usage of onboard storage and downlink bandwidth. CCSDS recommended 123.0-B-1 standard[2] has been released with onboard compression scheme of hyperspectral data. The scheme is based on Fast Lossless algorithm and consists of two main functional blocks namely Predictor and Encoder. Predictor algorithm can be implemented in two modes 'Full Neighborhood Oriented' and 'Reduced Column Oriented'. Encoder algorithm also defines two options 'sample-adaptive' and 'block-adaptive'. We have developed a MATLAB based model implementing the compression scheme with all options defined by the standard. Decompression model is also developed for getting back actual data and end to end verification. Four sets of HySI data (AVIRIS, Hyperion, Chandrayan-1 and FTIS) have been applied as input to the developed model for evaluation of the model. Compression ratio achieved is between 2 to 3 and lossless compression is ensured for each set of data as Mean Square Error (MSE) is zero for all hyperspectral images. Also visual reconstruction of decompressed data matches with original ones. In this paper we have discussed algorithm implementation methodology and results.
Fast Lossless, MATLAB, Hyperspectral, Image Compression
Короткий адрес: https://sciup.org/15013553
IDR: 15013553
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