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长春光学精密机械与物... [1]
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会议论文 [1]
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2012 [1]
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On hyperspectral remotely sensed image classification based on MNF and AdaBoosting (EI CONFERENCE)
会议论文
2012 3rd IEEE/IET International Conference on Audio, Language and Image Processing, ICALIP 2012, July 16, 2012 - July 18, 2012, Shanghai, China
Xu Y.
;
Yu P.
;
Guo B.
;
Gao X.
;
Guo Y.
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提交时间:2013/03/25
As an effective statistical learning tool
AdaBoosting has been widely used in the field of pattern recognition. In this paper
a new method is proposed to improve the classification performance of hyperspectral images by combining the minimum noise fraction (MNF) and AdaBoosting. Because the hyperspectral imagery has many bands which have strong correlation and high redundancy
the hyperspectral data are pre-processed by the minimum noise fraction to reduce the data's dimensionality
whilst to remove noise bands simultaneously. Then
we use an AdaBoost algorithm to conduct the classification of hyperspectral remotely sensed image. Experimental results show that the classification accuracy is improved and the time of calculation is reduced as well. 2012 IEEE.
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