A Novel Coding Algorithm for Interference Hyper-Spectral Images Based on Classification and Curve-Fitting

Interference spectral images have their own features. The correlations between the spectral lines are weak. And the data in a spectral line have particularity, that is, the data vary abruptly in the main district and the rest data change monotonously. On the basis of analyzing the particularity, a novel method for data classification is proposed. The data of a spectral line are decomposed to two classes, called main-interference class and no main-interference class. And a similarity-based match method is presented for the data of main-interference class, while the data of no main-interference class is processed by another method called 2-order curve-fitting algorithm. The data of a spectral line can be approached appropriately in the ways discussed above, which avails for image compression. The experimental results show that the output rate decreases by 0.2-0.4bpp for lossless compression and also can improve the loss compression efficiency.