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Hyperspectral data compression by using rational function curve fitting in spectral signature subintervals and Savitsky-Golay smoothing filter

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Abstract

There are various methods for lossless and lossy compression of hyperspectral images, acting in the spatial or spectral domain. Regarding the importance of spectral information in hyperspectral images, compression should be done in the way that this kind of information is well preserved. Compression methods are either of the predictive function or using of codebooks. Some transformation coding are discrete cosine transform (DCT), discrete wavelet transform (DWT), or principal component analysis (PCA), that is one of the most effective ways to eliminate image correlations and reduce their volume. In this manuscript a curve fitting based method is applied exclusively to compress hyperspectral images. This method concentrates on the spectral signature of each pixel of a hyperspectral image to reduce the features by finding the closest approximation function to express the curve and storing its coefficients as new features. The algorithm can be implemented pixel by pixel, hence increases the speed of compression process. This method has good results alongside previous methods such as PCA. However, the approximate curve has severe distortion in some points. In this paper, we try to improve approximation and eliminate these distortions by different approaches such as finding the place of distortions and breakdown the SCR to a number of sub intervals, using Savitsky-Golay smoothing filter, or combining these techniques. The proposed methods, besides eliminating the distortion, dramatically improved PSNR of the reconstructed image. Experiments have been performed by using three well-known hyperspectral data sets and results demonstrate the power of the suggested methods.

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Authors and Affiliations

  1. Department of Communication, College of Electrical Engineering, Yadegar-e-Imam Khomeini (RAH), Shahr-e-Rey Branch, Islamic Azad University, Tehran, Iran

    Mersedeh Beitollahi, S. Abolfazl Hosseini & Sayed A. Hadei

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  1. Mersedeh Beitollahi
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  2. S. Abolfazl Hosseini
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  3. Sayed A. Hadei
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Correspondence to S. Abolfazl Hosseini.

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We the undersigned declare that this manuscript is original, has not been full or partly published before, and is not currently being considered for publication elsewhere.

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We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by the undersigned. We understand that the Corresponding Author is the sole contact for the editorial process. The corresponding author “S. Abolfazl Hosseini” is responsible for communicating with the other authors about process, submissions of revisions, and final approval of proofs.

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Communicated by: H. Babaie

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Beitollahi, M., Hosseini, S.A. & Hadei, S.A. Hyperspectral data compression by using rational function curve fitting in spectral signature subintervals and Savitsky-Golay smoothing filter. Earth Sci Inform 15, 1215–1232 (2022). https://doi.org/10.1007/s12145-022-00796-6

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