Using Savitsky-Golay filter and interval curve fitting in order to hyperspectral data compression

Hyperspectral (HS) Images because of simultaneous acquisition of data on hundreds of narrow and close spectral bands have a high degree of inter-band correlation. For this reason and the large volume of this type of data we have to compress them. There are various lossy / lossless compressing methods acting on spectral and / or spatial domain. In this paper a lossy compressing method is proposed based on rational function curve fitting of spectral reflectance curve (SRC) intervals of image pixels after smoothing themutilizing Savitsky-Golay (SG) filter. The results demonstrate good performance in comparison to the other methods of compressing, including principal components analysis (PCA) as a bench mark method in this area. The degree of S-G smoothing filter and its window length changes in order to achieve the best performance. Also, we propose a special method for dividing SRCs into a number of adjacent non-overlapping intervals. Each of these sections is fitted by a variety of rational fraction curves, individually. The best fitted curve coefficients will be used as the new data cube.