| 光谱学与光谱分析 |
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| A New Method of Accurately Extracting Spectral Values for Discrete Sampling Points |
| Lü Zhen-zhen, LIU Guang-ming*, YANG Jin-song* |
| State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China |
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Abstract In the establishment of remote sensing information inversion model, the actual measured data of discrete sampling points and the corresponding spectrum data to pixels of remote sensing image, are used to establish the relation, thus to realize the goal of information retrieval. Accurate extraction of spectrum value is very important to establish the remote sensing inversion mode. Converting target spot layer to ROI (region of interest) and then saving the ROI as ASCII is one of the methods that researchers often used to extract the spectral values. Analyzing the coordinate and spectrum values extracted using original coordinate in ENVI, we found that the extracted and original coordinate were not inconsistent and part of spectrum values not belong to the pixel containing the sampling point. The inversion model based on the above information cannot really reflect relationship between the target properties and spectral values; so that the model is meaningless. We equally divided the pixel into four parts and summed up the law. It was found that only when the sampling points distributed in the upper left corner of pixels，the extracted values were correct. On the basis of the above methods, this paper systematically studied the principle of extraction target coordinate and spectral values, and summarized the rule. A new method for extracting spectral parameters of the pixel that sampling point located in the environment of ENVI software. Firstly, pixel sampling point coordinates for any of the four corner points were extracted by the sample points with original coordinate in ENVI. Secondly, the sampling points were judged in which partition of pixel by comparing the absolute values of difference longitude and latitude of the original and extraction coordinates. Lastly, all points were adjusted to the upper left corner of pixels by symmetry principle and spectrum values were extracted by the same way in the first step. The results indicated that the extracted spectrum values of all points were accurate. Experiment on OLI (Operational Land Imager), TM and ETM+ images showed that this method can accurately extract the discrete spectrum value, and as well, clear principle, simple and feasible operation, strong applicability, This paper provides a new idea for remote sensing image extraction of discrete point spectrum.
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Received: 2014-06-27
Accepted: 2014-10-15
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Corresponding Authors:
LIU Guang-ming,YANG Jin-song
E-mail: gmliu@issas.ac.cn;jsyang@issas.ac.cn
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[1] Dogan H M, Kl O M. International Journal of Remote Sensing, 2013, 34 (15): 5623.
[2] Bannari A, Guedon A M, El-Harti A, et al. Archives of Agronomy and Soil Science, 2013, 59(4): 471.
[3] Seyed R F S, Sanaz Z, Seyed A A. Archives of Agronomy and Soil Science, 2013, 59 (4): 471.
[4] Dogan, H M, Kl, O M. International Journal of Remote Sensing, 2013, (15): 5623.
[5] LIU Huan-jun, ZHAO Chun-jiang, WANG Ji-hua, et al(刘焕军, 赵春江, 王纪华, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2011, (8): 211.
[6] WANG Rui-yan, YU Zhen-wen, XIA Yan-ling, et al(王瑞燕, 于振文, 夏艳玲, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2013, 33(10): 2809.
[7] Bannari A, Guedon A M, El-Harti A, et al. Communications in Soil Science and Plant Analysis, 2008, 39(19-20): 2795.
[8] CHU Zhong-xin, SUN Xiao-gong, ZHANG Jing(褚忠信, 孙效功, 张 晶). Computer Development & Applications(电脑开发与应用), 2003, (10): 25.
[9] DENG Shu-bin(邓书斌). Remote Sensing Image Processing Method of ENVI(ENVI遥感图像处理方法). Beijing: Science Press(北京：科学出版社)，2010.
[10] Jabbar M, Zhou J. Journal of Earth Science, 2012, 23(6): 881. |
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