Interband prediction of hyperspectral images using generalized regression neural network

Predicting upcoming bands of hyperspectral images is an important task in modern image compression algorithms. This paper proposes a new algorithm to predict the band-wise correlation of hyperspectral images based on a generalized regression neural network (GRNN). The proposed algorithm uses the intensity values of the previous bands to train the GRNN and approximates the correlation between them. The next band is then predicted using the trained network and the immediately previous band. This algorithm works on a pixel-by-pixel basis and does not involve any mathematical modeling or any previous knowledge of the images. The performance of the proposed algorithm is evaluated by applying it to several Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) reflectance datasets. Simulation results show that the proposed algorithm provides substantial accuracy in the prediction of upcoming bands.