An Adaptive Spectral Decorrelation Method for Lossless MODIS Image Compression

Spectral decorrelation has been considered an important approach in multispectral image compression to remove the redundancy among bands. In this paper, we propose a novel adaptive spectral decorrelation method based on clustering analysis for Moderate Resolution Imaging Spectroradiometer (MODIS) image compression. The remote sensing image bands are divided into different clusters by the method based on density peak clustering. Then, reversible Karhunen-Loeve transform and polynomial least square estimation are employed to reduce the band redundancy, achieving an effective spectral decorrelation. As shown by the experimental results, our method achieves remarkable bit-saving when compared to the state-of-the-art algorithms on MODIS image data set.