Aerial image information extraction based on non-negative matrix factorization

HAO Hong; XU Chang-qing; ZHANG Xin-ping

doi:10.11833/j.issn.2095-0756.2012.01.013

Volume 29 Issue 1

Feb. 2012

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HAO Hong, XU Chang-qing, ZHANG Xin-ping. Aerial image information extraction based on non-negative matrix factorization[J]. Journal of Zhejiang A&F University, 2012, 29(1): 72-77. doi: 10.11833/j.issn.2095-0756.2012.01.013

Citation:

HAO Hong, XU Chang-qing, ZHANG Xin-ping. Aerial image information extraction based on non-negative matrix factorization[J]. Journal of Zhejiang A&F University, 2012, 29(1): 72-77. doi: 10.11833/j.issn.2095-0756.2012.01.013

Aerial image information extraction based on non-negative matrix factorization

doi: 10.11833/j.issn.2095-0756.2012.01.013

1.
School of Environmental and Resource Sciences，Zhejiang A & F University，Lin’an 311300，Zhejiang，China

More Information

Corresponding author: XU Chang-qing
Received Date: 2011-02-28
Rev Recd Date: 2011-04-07
Publish Date: 2012-02-20

Abstract

This study was on superiority of the non-negative matrix factorization （NMF） algorithm for application of information extracted with aerial images. First，NMF was used for aerial image information extraction，and then this data was compared with a principal component analysis （PCA） in which r （the number of rows or columns of basic matrix） and Eignum（the number of eigenvalues） were given different values. Experimental results showed that the run time of NMF with r = 20 or 50 was less than that of PCA with an Eignum = 20 or 50. Also，the recognition rate of NMF with r = 50 was higher than that of an Eignum = 50. The experiment showed that non-negative matrix factorization had advantages of a short time period with a high recognition rate. ［Ch，10 fig. 2 tab. 7 ref.］
- forest management,
- non-negative matrix factorization （NMF）,
- aerial image,
- principle component analysis （PCA）,
- eigenvalue

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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

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Aerial image information extraction based on non-negative matrix factorization

1. School of Environmental and Resource Sciences，Zhejiang A & F University，Lin’an 311300，Zhejiang，China

Corresponding author: XU Chang-qing

Received Date: 2011-02-28

Rev Recd Date: 2011-04-07

Publish Date: 2012-02-20

Key words:

forest management /
non-negative matrix factorization （NMF） /
aerial image /
principle component analysis （PCA） /
eigenvalue

Abstract: This study was on superiority of the non-negative matrix factorization （NMF） algorithm for application of information extracted with aerial images. First，NMF was used for aerial image information extraction，and then this data was compared with a principal component analysis （PCA） in which r （the number of rows or columns of basic matrix） and Eignum（the number of eigenvalues） were given different values. Experimental results showed that the run time of NMF with r = 20 or 50 was less than that of PCA with an Eignum = 20 or 50. Also，the recognition rate of NMF with r = 50 was higher than that of an Eignum = 50. The experiment showed that non-negative matrix factorization had advantages of a short time period with a high recognition rate. ［Ch，10 fig. 2 tab. 7 ref.］

Aerial image information extraction based on non-negative matrix factorization

doi: 10.11833/j.issn.2095-0756.2012.01.013

Abstract

References

Proportional views

通讯作者: 陈斌, bchen63@163.com

Related

Proportional views