Lossless hyperspectral image compression based on prediction

Hyperspectral imaging technology plays an important role in the field of remote sensing applications. Hyperspectral images exhibit significant spectral correlation whose exploitation is crucial for compression. In this paper an efficient method for Hyperspectral image compression is presented based on differential prediction with very low complexity. The proposed scheme consists of a difference coder, two predictors and a Huffman codec. The processing of the pixels varies depending on their position in the image. The resulting difference between the predicted and the actual pixel values are encoded into variable-length codewords using the Huffman codebook. The performance of the proposed algorithm has been evaluated on AVIRIS images. The experimental results show that with a Compression Ratio (CR) up to 4.14, the proposed method provides a competitive performance with comparison of JPEG2000, JPEG-LS and the OCC schemes.