Volume 31 Issue 10
Oct.  2005
Turn off MathJax
Article Contents
Du Peng, Zhao Huijie. Noise robust ICA feature extraction algorithm for hyperspectral image[J]. Journal of Beijing University of Aeronautics and Astronautics, 2005, 31(10): 1101-1105. (in Chinese)
Citation: Du Peng, Zhao Huijie. Noise robust ICA feature extraction algorithm for hyperspectral image[J]. Journal of Beijing University of Aeronautics and Astronautics, 2005, 31(10): 1101-1105. (in Chinese)

Noise robust ICA feature extraction algorithm for hyperspectral image

  • Received Date: 15 Jun 2004
  • Publish Date: 31 Oct 2005
  • Feature extraction is important to hyperspectral imagery processing in that it can distinguish special featured object from background clutter and remove redundant information. An ICA(independent component analysis) based on the feature extraction algorithm for hyperspectral remote sensing data is proposed. In order to handle the over-sensitivity of ICA to noise and data imperfection, the MNF(maximum noise fraction) is adopted as the replacement of conventional principal component analysis. The UICA(undercomplete ICA) led by the MNF not only raises the time efficiency, but also maintains the extracting ability of ICA. The performance of the algorithm is verified by the results of HYIDCE and PHI experiments.

     

  • loading
  • [1] eil H Timm. Applied Multivariate Analysis[M] Springer, 2002 [2] adjudin Saldju, Landgrebe David. Classification of high dimensional data with limited training [EB/OL] ttp://dynamo.ecn.purdue.edu/~landgreb/Saldju_TR.pdf [3] su H P, Tseng H Y. Feature extraction for hyperspectral image[A] Proc. 20th ACRS[C] Hong Kong, 1999,1:405~410 [4] yv[AKa¨] nen A, J Karhunen, Oja E. Independent component analysis[M] iley, 2001 [5] tefan A Robila, Pramod K Varshney. Target detection in hyperspectral images based on independent component analysis[A] Proc. SPIE Int. Soc. Opt. Eng[C] Orlando, USA, 2002 [6] hiang ShaoShan, Chang CheinI, Ginsberg I W. Unsupervised hyperspectral image analysis using independent component analysis [A] Geoscience and Remote Sensing Symposium, 2000 Proceedings. IGARSS 2000 IEEE 2000 International Vol.7[C] 2000. 3136~3138 [7] hah C A, Arora M K, Robila S A, et al. ICA mixture model based unsupervised classification of hyperspectral imagery [A] 31st Applied Imagery Pattern Recognition Workshop, 2002. Proceedings[C] 2002. 29~35 [8] reen A A, Berman M, Switzer P, et al. A transformation for ordering multispectral data in terms of image quality with implications for noise removal[J] Geoscience and Remote Sensing, IEEE Transactions on, 1988, 26(1):65~74 [9] ee J B, Woodyatt A S, Berman M. Enhancement of high spectral resolution remotesensing data by a noiseadjusted principal components transform[J] Geoscience and Remote Sensing, IEEE Transactions on, 1990, 28(3):295~304 [10] heriyadat A, Bruce L M. Why principal component analysis is not an appropriate feature extraction method for hyperspectral data [A] Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. 2003 IEEE International.Vol.6[C] 2003. 3420~3422 [11] over M T, Thomas J A. Elements of information theory[M] John Wiley & Sons, 1991 [12] yvarinen A. Fast and robust fixedpoint algorithms for independent component analysis[J] Neural Networks, IEEE Transactions on, 1999, 10(3):626~634
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views(3310) PDF downloads(1466) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return