Hyperspectral Data Compression by Using Subintervals Curve Fitting, and Smoothing Filter

Hyperspectral images due to simultaneous acquisition of data in more than hundreds narrow and close spectral bands, have a very high correlation bandwidth. Hence, in order to store in less storage space, higher transmission speed and less bandwidth, they need compression. Various lossless and lossy methods for compression are exist, that can be in the spatial domain or in the spectrum domain. But, regard to the importance of spectral information of hyperspectral images in remote sensing, this compression should be done by this condition that the spectral information of this kind of images is well preserved. Compression methods can be based on either the predictive function or using of a codebook, to compress information. Data compression can also be done based on transformation coding, which these transformations can be cosine functions (DCTs), wavelet functions (DWTs), or principal component analysis (PCAs). Of course, PCA-based compression is one of the most effective ways to eliminate image correlations and reduce their volume. Another extension is the method of using curve fitting, which is applied exclusively to compress hyperspectral images due to its effect on the image spectrum. This method uses the spectral signature of the each pixel of image to reduce the feature by finding the closest approximation function to express the curve and storing its coefficients as a new feature for reconstruction compressed data. By replacing these coefficients in the equation of approximation, spectrum reflection curve for each pixel can be reconstructed. This method has very good results in comparison with previous methods such as PCA, but in compression using this method, the SRC curve has been approximated in some points with distortion. In this paper, we tried to eliminate these distortions, by finding points which have distortion and Breakdown the SCR. On the other hand, by using the Savitsky-Golay smoothing filter we can also reduce distortion and increase the PSNR. Another way to eliminate or reduce this distortion described in this article is as follow: At the first the spectral signature of each pixel of the intended data is smoothed by a Savitsky-Golay smoothing filter and then by using a particular method is divided into adjoining adjacent spaces and then a curve is plotted for each slice of data. By choosing the best degree and window length for smoothing and selecting the best degree of numerator and denominator of function, the coefficients of the selected rational function are considered as new features of the image. By using the proposed method, in addition to eliminating the distortion, the PSNR level is became much higher and the reconstructed image quality is very close to the original image.