Clustered linear prediction for lossless compression of hyperspectral images using adaptive prediction length

This paper extends clustered differential pulse code modulation (C-DPCM) lossless compression method for hyperspectral images. In C-DPCM method 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 proposed use of adaptive prediction length is shown have lower bits/pixel value than the original C-DPCM method for new AVIRIS test images. Both calibrated are uncalibrated images showed improvement over fixed prediction length.