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REMOTE SENSING FOR LAND & RESOURCES    2017, Vol. 29 Issue (1) : 104-109     DOI: 10.6046/gtzyyg.2017.01.16
Technology and Methodology |
Manifold alignment for dimension reduction and classification of multitemporal hyperspectral image
LU Jintao, MA Li
School of Mechanical Engineering and Electronic Information, China University of Geosciences(Wuhan), Wuhan 430074, China
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Abstract  

For multitemporal hyperspectral images, the spectral characteristics of the same land cover object may vary significantly. Therefore, manifold alignment algorithm was employed to find a feature space in which data distributions of both images become the same. The method includes three steps. Firstly, a standard linear or nonlinear dimension reduction method is used to reduce the dimensionality of hyperspectral images. Secondly, the Procrustes analysis method is utilized to remove the translational, rotational and scaling components from one set so that the optimal alignment between the two data sets can be achieved. Finally, the nearest neighbor algorithm is applied for classification. Experimental results using multitemporal hyperion images demonstrate that the proposed approach can obtain performances which are superior to those of several popular manifold alignment methods.

Keywords WorldView2 data      rock spectral characteristic      remote sensing lithological information enhancement      spectral characteristic      spatial characteristics     
:  TP751.1  
Issue Date: 23 January 2017
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WANG Pingping
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WANG Pingping,TIAN Shufang. Manifold alignment for dimension reduction and classification of multitemporal hyperspectral image[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(1): 104-109.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2017.01.16     OR     https://www.gtzyyg.com/EN/Y2017/V29/I1/104

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