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Feature correlation based parallel hyper spectral image compression using a hybridization of FCM and subtractive clustering

  • Theory and Methods of Signal Processing
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Abstract

This paper presents the GLCM based texture feature extraction for all the bands of Hyper Spectral Images (HSIs) and then Correlation Coefficient (CC) is estimated. Based on the CC, the threshold is computed and the bands are segregated into high correlation bands and low correlation bands. The bands with high correlation is compressed using the residual band information technique. Subsequently, the Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) approaches are employed for compressing the low correlation bands. An efficient HSI compression approach is introduced based on Discrete Wavelet Transform (DWT) for the initial band of HSI. This exploits the information of both spectral and spatial in the images. Accordingly, the compressed output is decoded and reconstructed. A novel hybridization of the Fuzzy C-Means (FCM) and Subtractive Clustering (SC) is proposed to cluster the reconstructed images. Therefore, the FCM provides the optimum results, whereas the SC finds the accurate number of clusters. The experimental results exhibit the better performance of memory consumption, MSE, PSNR, and compression ratio than the existing methods.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Sri Subramanya College of Engineering and Technology, Palani, Tamil Nadu, India

    S. Kala

  2. Professor and Head of the Department of Electronics and Communication Engineering, Velammal College of Engineering and Technology, Madurai, Tamil Nadu, India

    S. Vasuki

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  1. S. Kala
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  2. S. Vasuki
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Correspondence to S. Kala.

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Kala, S., Vasuki, S. Feature correlation based parallel hyper spectral image compression using a hybridization of FCM and subtractive clustering. J. Commun. Technol. Electron. 59, 1378–1389 (2014). https://doi.org/10.1134/S1064226914120195

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  • DOI: https://doi.org/10.1134/S1064226914120195

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