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Remote Sensing for Land & Resources    2018, Vol. 30 Issue (2) : 80-86     DOI: 10.6046/gtzyyg.2018.02.11
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Multi-manifold LE algorithm for dimension reduction and classification of multitemporal hyperspectral image
Dongyang WU(), Li MA()
School of Mechanical Engineering and Electronic Information,China University of Geosciences(Wuhan), Wuhan 430074, China
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Abstract  

The traditional manifold learning algorithms are based on the assumption that categories of data are located in the same manifold structure; nevertheless, due to the different features of different data categories, it is more reasonable that the data are in respective different manifold structures. Hence, the assumption of multi-manifold is more applicable for data classification. This paper adopts the thought of multi-manifold spectral clustering algorithm, mainly focuses on multiple manifolds LE algorithm, and applies this algorithm to the processing of hyperspectral data. Combined with the features of the hyperspectral data, the multiple manifolds LE algorithm is further improved by adding the spatial information and data maker information. The experimental results show that, in many kinds of hyperspectral data, the multi-manifold LE algorithm has higher precision than the LE algorithm. In addition, the improved multi-manifold LE algorithm could classify data with higher precision than the LE algorithm and multi-manifold LE algorithm. The authors have reached the conclusion that the assumption of multi-manifold is in better agreement with the features of hyperspectral data and the improved algorithm is of high performance.
Keywords manifold learning      multi-manifold hypothesis      hyperspectral data      classification     
:  TP79  
Corresponding Authors: Li MA     E-mail: crazywdy@163.com;maryparisster@gmail.com
Issue Date: 30 May 2018
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Dongyang WU
Li MA
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Dongyang WU,Li MA. Multi-manifold LE algorithm for dimension reduction and classification of multitemporal hyperspectral image[J]. Remote Sensing for Land & Resources, 2018, 30(2): 80-86.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.02.11     OR     https://www.gtzyyg.com/EN/Y2018/V30/I2/80
Fig.1  Comparion of spectral graph with spatial information added before and after
Fig.2  Assign classes to blocks
算法 训练数据比例
10% 30% 50%
LE 0.666 0.742 0.766
MLE 0.706 0.783 0.800
MLE_Spatial 0.875 0.915 0.929
MLE_Spatial_Label 0.920 0.954 0.970
Tab.1  Optimal accuracy of different algorithms on BOT data
算法 训练数据比例
10% 30% 50%
LE 0.754 0.796 0.812
MLE 0.756 0.806 0.821
MLE_Spatial 0.834 0.880 0.893
MLE_Spatial_Label 0.865 0.913 0.918
Tab.2  Optimal accuracy of different algorithms on KSC data
算法 训练数据比例
10% 30% 50%
LE 0.646 0.667 0.670
MLE 0.670 0.720 0.731
MLE_Spatial 0.730 0.770 0.786
MLE_Spatial_Label 0.771 0.845 0.846
Tab.3  Optimal accuracy of different algorithms on PU data
Fig.3  Classification accuracy of four kinds of algorithms in different kinds of data
纯净点数 块数
40 80 120 160 200 240
未添加空间信息时 275 418 558 689 758 853
添加空间信息时 654 1 047 1 254 1 392 1 480 1 502
  
纯净点数 块数
20 60 100 140 180 220
未添加空间信息时 484 565 655 814 879 949
添加空间信息时 564 952 1 105 1 231 1 284 1 357
  
纯净点数 块数
100 150 200 250 300
未添加空间信息时 313 434 554 683 793
添加空间信息时 812 1 078 1 294 1 460 1 564
  
Fig.4  Accuracy under different blocks
Fig.5  Distribution of same kind of data in two bands before and after spatial information added
Fig.6  Accuracy of classification with different values of the neighborhood
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