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Volume 29 Issue 12
Jan.  2011
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Feng Yan, He Ming-yi, Song Jiang-hong, Wei Jiang. ICA-Based Dimensionality Reduction and Compression of Hyperspectral Images[J]. Journal of Electronics & Information Technology, 2007, 29(12): 2871-2875. doi: 10.3724/SP.J.1146.2006.00735
Citation: Feng Yan, He Ming-yi, Song Jiang-hong, Wei Jiang. ICA-Based Dimensionality Reduction and Compression of Hyperspectral Images[J]. Journal of Electronics & Information Technology, 2007, 29(12): 2871-2875. doi: 10.3724/SP.J.1146.2006.00735

ICA-Based Dimensionality Reduction and Compression of Hyperspectral Images

doi: 10.3724/SP.J.1146.2006.00735
  • Received Date: 2006-05-29
  • Rev Recd Date: 2006-12-20
  • Publish Date: 2007-12-19
  • This paper proposes a dimensionality reduction and compression method of hyperspectral images based on Independent Component Analysis(ICA) for hyperspectral image analysis. At first hyperspectral features are extracted using ICA and dimensionality reduction is accomplished. Then, dimensionality reduction images are compressed by the predictive code and adaptive arithmetic code. The experimental results by using 220 bands and 64 bands hyperspectral data show that the method achieved higher compression ratio, more strong analysis capability and lower peak signal-to-noise ratio than dimensionality reduction based on Principal Components Analysis(PCA).
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  • Kaarna A, Zemcik P, and iainen H, et al.. Compression of multispectral remote sensing images using clustering and spectral reduction. IEEE Trans. on Sci. Remote Sensing, 2000, 38(2): 1588-1592.[2]吴家骥,吴成柯. Karhunen-Loeve和小波变换的多光谱图像三维集合嵌入块编码压缩算法[J].电子与信息学报.2005,27(8):1244-1247浏览[3]闫敬文,沈贵明. 基于三维KLT/WT/WTVQ的多光谱数据压缩方法. 厦门大学学报,2001, 40(5): 1051-1055. Yan Jing-wen and Shen Gui-ming. A method for 3D multispectral data compression based on KLT/WT/WTVQ. Journal of Xiamen University, 2001, 40(5): 1051-1055.[4]张绍荣,苏令华. 一种基于主成分分析的高光谱图像压缩方法. 无线电工程,2005, 35(9): 53-54. Zhang Shao-rong and Su Ling-hua. A compression method of hyperspectral images based on PCA. Radio Engineering, 2005, 35(9): 53-54.[5]Ramakrishna B, Wang Jing, and Chang C I, et al.. Spectral/spatial hyperspectral image compression in conjunction with virtual dimensionality. Algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery XI, Proc. 5806, SPIE, 2005: 772-781.[6]Robila S A and Varshney P K. A fast source separation algorithm for hyperspectral image processing. IEEE International Conference on Geoscience and Remote Sensing, Toronto, Canada, 2002, 6: 3516-3518.[7]Lennon1 M, Mercier1 G, and Mouchot1 M C, et al.. Independent component analysis as a tool for the dimensionality reduction and the representation of hyperspectral images. IEEE International Conference on Geoscience and Remote Sensing, Sydney, Australian, 2001, 6: 2893-2895.[8]Hyvarinen A. Fast and robust fixed-point algorithms for independent component analysis[J].IEEE Trans. on Neural Networks.1999,10(3):626-634
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