Title :
Clustered DPCM for the lossless compression of hyperspectral images
Author :
Mielikainen, Jarno ; Toivanen, Pekka
Author_Institution :
Dept. of Inf. Technol., Lappeenranta Univ. of Technol., Finland
Abstract :
A clustered differential pulse code modulation lossless compression method for hyperspectral images is presented. The spectra of a hyperspectral image is clustered, and an optimized predictor is calculated for each cluster. Prediction is performed using a linear predictor. After prediction, the difference between the predicted and original values is computed. The difference is entropy-coded using an adaptive entropy coder for each cluster. The achieved compression ratios presented here are compared with those of existing methods. The results show that the proposed lossless compression method for hyperspectral images outperforms previous methods.
Keywords :
adaptive codes; data compression; differential pulse code modulation; entropy codes; geophysical signal processing; image coding; prediction theory; remote sensing; adaptive entropy coder; clustered DPCM; clustered differential pulse code modulation lossless compression method; compression ratios; entropy-coding; hyperspectral images; linear predictor; lossless compression; optimized predictor; Discrete cosine transforms; Entropy; Hyperspectral imaging; Hyperspectral sensors; Image coding; Modulation coding; Partitioning algorithms; Pulse modulation; Vector quantization; Wavelet transforms;
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
DOI :
10.1109/TGRS.2003.820885