Ding Ling, Tang Ping, Li Hongyi. Analysis on spectral unmixing based on manifold learning[J]. Infrared and Laser Engineering, 2013, 42(9): 2421-2425.
Citation:
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Ding Ling, Tang Ping, Li Hongyi. Analysis on spectral unmixing based on manifold learning[J]. Infrared and Laser Engineering, 2013, 42(9): 2421-2425.
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Analysis on spectral unmixing based on manifold learning
- 1.
Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing 100101,China;
- 2.
University of Chinese Academy of Sciences,Beijing 100049,China
- Received Date: 2013-01-04
- Rev Recd Date:
2013-02-15
- Publish Date:
2013-09-25
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Abstract
The main study on spectral unmixing is to develop a regression between mixed spectral features of main land-cover types and their responding fractional cover. Studying on in situ spectral reflectance data, based on one of the best known algorithms of manifold learning, locally linear embedding (LLE), a new modeling method named constrained least squares locally linear weighted regression(CLS-LLWR) was proposed. Spectral reflectance of four kinds of the mixed land-cover types in different percentages was measured and preliminarily analyzed. The model CLS-LLWR was verified by predicting fractional cover of main land- cover types. Compared with principal component regression (PCR) and partial least squares regression(PLSR), through comparison and analysis of the standard error of prediction(SE), the result shows that the CLS-LLWR has better predictability. This study indicates that manifold study has the potential for the information extraction of mixed land cover types in hyperspectral image.
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References
[1]
|
Tong Qingxi, Zhang Bing, Zheng Lanfen. Hyperspectral Remote Sensing Principle, Technique and Application[M].Beijing: Higher Education Press, 2006. (in Chinese) 童庆禧, 张兵, 郑兰芬. 高光谱遥感原理、技术与应用[M]. 北京: 高等教育出版社, 2006. |
[2]
|
|
[3]
|
|
[4]
|
Atkinson P M, Cutler M E J, Lewis H. Mapping sub-pixel proportional landcover with AVHRR imagery[J]. International Journal of Remote Sensing, 1997, 18(4): 917-935. |
[5]
|
Marsh S E, Switzer P, Kowalik W S. Resolving the percentage of component terrains within single resolution elements[J]. Photogrammetric Engineering and Remote Sensing, 1980, 46(8): 1079-1086. |
[6]
|
|
[7]
|
|
[8]
|
Xu Jun, Hu Bingliang, Feng Dazheng, et al. Decoding method for the spectral mixing pixels in Hadamard transform spectral imager[J]. Infrared and Laser Engineering, 2012, 41(6): 1528-1531. (in Chinese) 徐君, 胡炳樑, 冯大政, 等. 哈达玛变换成像光谱仪中光谱混合像素点的解码方法[J]. 红外与激光工程, 2012, 41(6): 1528-1531 |
[9]
|
|
[10]
|
Wang Qunming, Wang Liguo, Liu Danfeng, et al. Sub-pixel mapping for land class with linear features using least square support vector machine[J]. Infrared and Laser Engineering,2012, 41(6): 1670-1675. (in English) 王群明, 王立国, 刘丹丹, 等. 基于最小二乘支持向量机的线性特征地物亚像元定位[J].红外与激光工程, 2012, 41(6): 1670-1675. |
[11]
|
|
[12]
|
Roweis S, Saul L. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 290: 2323-2326. |
[13]
|
Tenenbaum J B, Silva V D, Langford J C. A global geometric framework for nonlinear dimensionality reduction. Science[J]. Science, 2000, 290: 2319-2322. |
[14]
|
|
[15]
|
William S Cleveland, Susan J Devlin. Locally weighted regression: An approach to regression analysis by local fitting[J]. Journal of the American Statistical Association, 1988, 83(403): 596-610. |
[16]
|
|
[17]
|
|
[18]
|
Zhang Zhenyue, Zha Hongyuan. Principal manifolds and nonlinear dimensionality reduction Via tangent space alignment[J]. SIAM Journal of Scientific Computing, 2004, 26(1): 313-338. |
[19]
|
Xu Lu, Shao Xueguang. Methods of Chemometrics[M]. Beijing: Science Press, 2004. 许禄, 邵学广.化学计量学方法[M]. 北京: 科学出版社, 2004. |
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