Hyperspectral Image Lossless Compression Based on Optimal Linear Predictor
Jianshu Luo · Remote Sensing Information · 2005
Hyperspectral images are hard to compress because of their abundant details,complicated texture and insignificant spacial correlation.Making use of the significant spectral correlation within the hyperspectral images,we propose an optimal linear predictor which makes the square error minimal.Then we use lifting scheme and SPIHT algorithm to remove the spatial redundancy efficiently.Experiments show that the method works better than 3D-SPIHT algorithm and software WINRAR.