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| Visualization of Petroleum Hydrocarbon Content in Latosol Based on Hyperspectral Imaging Technology |
| CHEN Zhi-li1, LIU Qiang2，YIN Wen-qi2，LIU Hong-tao2，YANG Yi1 |
1. Department of Military Facilities, Army of Logistical University，Chongqing 401311, China
2. Department of Oil Engineering, Army of Logistical University，Chongqing 401311, China |
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Abstract Hyperspectral imaging technology is a rapid and nondestructive technique, which has the characteristic of combining the image and spectra, each band will represent an image, each pixel displays a spectra. Hyperpectral image can not only obtain spectral information of the samples, but also spatial information of objects representation, having great value in many field at present. In this paper, hyperspectral imaging technology is used to visualize the distribution of petroleum hydrocarbons in soil. A sample of latosol samples with different petroleum hydrocarbons content was prepared and divided into modeling samples and prediction samples. Hyperspectral images were collected. In order to avoid the interference of image background, the mask was used to eliminate the background. After extracting the average spectral region of interest modeling in the sample, the successive projection algorithm select characteristics variables. Based on the extracted feature variables, on one hand, a MLR prediction modelis established, On the other hand, the characteristics of hyperspectral image are extracted from the prediction sample. Finally, the data of the pixel on the characteristic image is substituted into the model to obtain the content distribution of the petroleum hydrocarbon. Through the method of image processing, the different contents are given different colors to realize the visualization of the distribution of petroleum hydrocarbon content in latosol. The research results show that using hyperspectral imaging technique and image processing method can realize the visualization of the distribution of petroleum hydrocarbon content in latosol, providing the basis for petroleum hydrocarbon content identification and inversion of soil in large scale.
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Received: 2017-09-18
Accepted: 2018-01-12
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[1] Dent D, Young A. Journal of Ecology, 1982, 70(3).
[2] Hauser A, Ali F, Al-Dosari B, et al. International Journal of Sustainable Development & Planning, 2013, 8(3): 413.
[3] Chakraborty S, Weindorf D C, Li B, et al. Science of the Total Environment, 2015, 514: 399.
[4] CHEN Zhi-li, YIN Wen-qi, LIU Hong-tao, et al(陈志莉, 尹文琦, 刘洪涛, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2017, 37(6): 1723.
[5] ZHANG Liang-pei, DU Bo, ZHANG Le-fei(张良培, 杜 博, 张乐飞). Hyperspectral Remote Sensing Images Processing(高光谱遥感影像处理). Beijing: Science Press(北京：科学出版社), 5.
[6] ZHU Feng-le, PENG Ji-yu, GAO Jun-feng, et al(朱逢乐，彭继宇，高峻峰，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2014, 30(23): 314.
[7] ZHANG Bing(张 兵). Journal of Remote Sensing(遥感学报)，2016，20(5)：1062.
[8] YU Lei, HONG Yong-sheng, ZHOU Yong, et al(于 雷, 洪永胜, 周 勇, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(13): 95. |
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