光谱学与光谱分析 |
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Spectral Uncertainty of Terrestrial Objects and the Applicability of Spectral Angle Mapper Algorithm |
CEN Yi1, 2, ZHANG Gen-zhong1, 3, ZHANG Li-fu1, LU Xu-hui4, ZHANG Fei-zhou5* |
1. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China 2. Key Laboratory of Geo-Special Information Technology,Ministry of Land and Resources,Chengdu University of Technology,Chengdu 610059, China 3. University of Chinese Academy of Sciences, Beijing 100049, China 4. Faculty of Information Engineering, China University of Geosciences(Wuhan), Wuhan 430074, China 5. School of Earth and Space Sciences, Peking Universiy, Beijing 100871, China |
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Abstract The spectral uncertainty of terrestrial objects causes a certain degree of spectral differences among feature spectra, which affects the accuracy of object recognition and also impacts the object recognition of spectral angle mapper algorithm (SAM). The spectral angle mapper algorithm is based on the overall similarity of the spectral curves, which was widely used in the classification of hyperspectral remotely sensed information. The spectral angle mapper algorithm does not take the spectral uncertainty of terrestrial objects into account while calculating the spectral angle between the spectral curves, and therefore does not tend to correctly identify the target objects. The applicability of the spectral angle mapper algorithm is studied for the spectral uncertainty of terrestrial objects and a modified SAM is proposed in this paper.In order to overcome the influence of the spectral uncertainty,the basic idea is to set a spectral difference value for the test spectra and the reference spectra and to calculate the spectral difference value based on derivation method according to the principle of minimum angle between the test spectra and the reference spectra. By considering the impact of the spectral uncertainty of terrestrial objects, this paper uses five kaolinite mineral spectra of USGS to calculate the spectral angle between the five kalinite mineral spectra by using local band combination and all bands to verify the improved algorithm. The calculation results and the applicability of the spectral angle mapper algorithm were analyzed. The results obtained from the experiments based on USGS mineral spectral data indicate that the modified SAM is not only helpful in characterizing and overcoming the impact of the spectral uncertainty but it can also improve the accuracy of object recognition to certain extent especially for selecting local band combination and has better applicability for the spectral uncertainty of terrestrial objects.
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Received: 2014-07-01
Accepted: 2014-11-05
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Corresponding Authors:
ZHANG Fei-zhou
E-mail: zhangfz@pku.edu.cn
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[1] TANG Pan-ke, WANG Run-sheng, YANG Su-ming(唐攀科, 王润生, 杨苏明). Geology And Prospecting(地质与勘探), 2006, 42(2): 74. [2] Hapke B. Journal of Geophysical Research: Solid Earth(1978—2012), 1981, 86(B4): 3039. [3] Cloutis E A. International Journal of Remote Sensing, 1996, 17(12): 2215. [4] Christopher Hecker, Mark Van der Meijde, et al. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(12): 4162. [5] ZHANG Liang-pei, ZHANG Li-fu(张良培, 张立福). Hyperspectral Remote Sensing(高光谱遥感). Wuhan: Wuhan University Press(武汉:武汉大学出版社), 2011. [6] TONG Qing-xi, ZHANG Bing, ZHENG Lan-fen, et al(童庆禧,张 兵,郑兰芬). Hyperspectral Remote Sensing: Principle, Technology and Application(高光谱遥感—原理, 技术与应用). Beijing: Higher Education Press(北京:高等教育出版社), 2006. [7] Baugh W M, Kruse F A, Atkinson Jr W W. Remote Sensing of Environment, 1998, 65(3): 292. [8] Fan F, Deng Y. International Journal of Applied Earth Observation and Geoinformation, 2014, 33: 290. [9] Li H, Lee W S, Wang K, et al. Precision Agriculture, 2014, 15(2): 162. [10] Park B, Windham W R, Lawrence K C, et al. Biosystems Engineering, 2007, 96(3): 323. [11] van der Meer F. Int. J. Appl. Earth Observation Geoinformation, 2006, 8(1): 3. [12] Crosta A P, Sabine C, Taranik J V. Remote Sensing of Environment, 1998, 65(3):309. [13] Lü Yin-liang, LI Shao-kun, WANG Ke-ru, et al(吕银亮, 李少昆, 王克如). Xinjiang Agricultural Sciences(新疆农业科学), 2011, 48(1): 1. [14] Gu Y, Wang C, Wang S, et al. Pattern Recognition Letters, 2011, 32(2): 114. |
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