Volume 39 Issue 5
May  2017
Article Contents
Article Navigation >  Journal of University of Electronic Science and Technology of China  > 2010  >  39(5): 711-715,730

XIN Qin, NIAN Yong-jian, WAN Jian-wei, HE Mi. Compression Technique for Hyperspectral Imagery Based on FastICA[J]. Journal of University of Electronic Science and Technology of China, 2010, 39(5): 711-715,730. doi: 10.3969/j.issn.1001-0548.2010.05.014
Citation: XIN Qin, NIAN Yong-jian, WAN Jian-wei, HE Mi. Compression Technique for Hyperspectral Imagery Based on FastICA[J]. Journal of University of Electronic Science and Technology of China, 2010, 39(5): 711-715,730. doi: 10.3969/j.issn.1001-0548.2010.05.014
shu

Compression Technique for Hyperspectral Imagery Based on FastICA

doi: 10.3969/j.issn.1001-0548.2010.05.014
  • 1.

    College of Electronic Science and Engineering,National University of Defense Technology Changsha 410073

  • Received Date: 2009-04-08
  • Rev Recd Date: 2009-11-25
  • Publish Date: 2010-10-15
  • Efficient compression for hyperspectral imagery has been the research focus for the developmentof remote sensing technique. The small targets information protection during the compression process without anypreknowledge should be necessarily considered. This paper presents a new lossy compression method forhyperspectral imagery based on fast independent component analysis (FastICA). Virtual dimensionality isintroduced to determine the number of target endmembers. The mixing matrix of FastICA is initialized by targetendmembers. Minimum noise fraction is employed for dimensionality reduction of original data volumes, andFastICA is performed on the selected principal components to generate independent components. Then, constantfalse alarm rate detection is performed on each IC, which is followed by morphologic filtering. Karhunen-Loevetransform is used to decorrelate the spectral redundancy, general scaling-based method is selected to upshift thewavelet coefficients of interested targets. Finally, each principle component is allocated optimal rate andcompressed by SPIHT algorithm. Experimental results on AVIRIS data show that the proposed method not onlyprovides high compression performance, but also preserves targets interested effectively.
  • 加载中
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
Get Citation
PDF
XML

Article Metrics

Article views(3601) PDF downloads(68) Cited by()

Related
Proportional views

Compression Technique for Hyperspectral Imagery Based on FastICA

doi: 10.3969/j.issn.1001-0548.2010.05.014
  • 1. College of Electronic Science and Engineering,National University of Defense Technology Changsha 410073
  • Received Date: 2009-04-08
  • Rev Recd Date: 2009-11-25
  • Publish Date: 2010-10-15

Abstract: Efficient compression for hyperspectral imagery has been the research focus for the developmentof remote sensing technique. The small targets information protection during the compression process without anypreknowledge should be necessarily considered. This paper presents a new lossy compression method forhyperspectral imagery based on fast independent component analysis (FastICA). Virtual dimensionality isintroduced to determine the number of target endmembers. The mixing matrix of FastICA is initialized by targetendmembers. Minimum noise fraction is employed for dimensionality reduction of original data volumes, andFastICA is performed on the selected principal components to generate independent components. Then, constantfalse alarm rate detection is performed on each IC, which is followed by morphologic filtering. Karhunen-Loevetransform is used to decorrelate the spectral redundancy, general scaling-based method is selected to upshift thewavelet coefficients of interested targets. Finally, each principle component is allocated optimal rate andcompressed by SPIHT algorithm. Experimental results on AVIRIS data show that the proposed method not onlyprovides high compression performance, but also preserves targets interested effectively.

XIN Qin, NIAN Yong-jian, WAN Jian-wei, HE Mi. Compression Technique for Hyperspectral Imagery Based on FastICA[J]. Journal of University of Electronic Science and Technology of China, 2010, 39(5): 711-715,730. doi: 10.3969/j.issn.1001-0548.2010.05.014
Citation: XIN Qin, NIAN Yong-jian, WAN Jian-wei, HE Mi. Compression Technique for Hyperspectral Imagery Based on FastICA[J]. Journal of University of Electronic Science and Technology of China, 2010, 39(5): 711-715,730. doi: 10.3969/j.issn.1001-0548.2010.05.014
shu

    HTML

Catalog

    Journal Online

    Author's Notes

    Links

    Copyright © 《电子科技大学学报》编辑部

    Address: Room 350, central building, University of Electronic Science and Technology, No.4, Section 2, Jianshe North Road, Chengdu Tel: 028-83202308,83207559 Email: xuebao@uestc.edu.cn

    本系统由 北京仁和汇智信息技术有限公司 开发 

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return