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An Enhanced Hyper Spectral Image (HSI) Compression Based On Residual Dependent Arithmetic Coder (RDAC)

[ Vol. 12 , Issue. 3 ]

Author(s):

Senthivel Thiyagarajan, Gnanadurai Dhavamani and Somasundaram Malathi   Pages 178 - 188 ( 11 )

Abstract:


Background: The Hyper Spectral Image (HSI) compression is a challenging and demanding task in many remote sensing applications, because it has the large hyperspectral data. Optical remote sensing is much increased due to newly imported sensor technologies and advancements. Lossy HSI compression is an essential part for long-terms spectral storage data. In this paper, we provide a new lossy HSI compression algorithm with the help of Residual Dependent Arithmetic Coder (RDAC).

Methods: The main intention of this work is to reduce the complexity while compressing the large volume of data by compressing the spectral bands. Here, the Gray Level Co-occurrence Matrix (GLCM) technique is employed to extract the texture features of the given HSI band image. Then, the k-means clustering algorithm is employed to select the reference band in each cluster based on the cluster prominence value. Moreover, the RDAC is used to compress the reference band and the residual band information of each cluster. Finally, the HSI is decompressed with the help of compressed HSI band images.

Results: In experiments, the performance of the proposed method is analyzed and evaluated in terms of Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR) and Compression Ratio (CR). Moreover, it is compared with some of the existing HSI compression techniques such as, Set Partitioning in Hierarchical Trees (SPIHT), Joint Photographic Expert Group (JPEG), Set Partitioning Embedded bloCK (3D-SPECK), Inverse Wavelet Transform (IWT) and Reverse Karhunen-Loeve Transform (RKLT).

Conclusion: This paper proposes a new RDAC technique for lossy HSI compression. For this purpose, different image processing techniques are used. In this analysis, it is proved that the proposed HSI compression technique provides the best results, when compared to the other techniques.

Keywords:

3D-SPIHT and 3D-SPECK, Bits Per Pixel (BPP), Compression Ratio (CR), Hyper Spectral Image (HSI), JPEG 2000, K-Means clustering, Mean Squared Error (MSE), Residual Dependent Arithmetic Coder (RDAC).

Affiliation:

Department of Computer Science and Engineering, J.P College of Engineering, Tenkasi, India.

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