融合光谱-纹理-颜色信息识别地下天然气微泄漏点

doi:10.3964/j.issn.1000-0593(2020)10-3123-07

Abstract
References
Related Citation (15)

Download: PDF (8029 KB)
Export: BibTeX | EndNote (RIS)

Abstract The leakage of underground natural gas storage will lead to serious safety accidents and economic losses, which can be effectively avoided by the rapid and accurate identification of natural gas leakage point. For the micro leakage of underground gas storage, traditional methods are difficult to be applied due to its low accuracy and high cost. In this study, hyperspectral technology is used to conduct field simulation experiments. Meanwhile, spectral, texture and color information was fused to identify natural gas leakage points. Based on the characteristics of vegetation growth in the corresponding areas under leakage stress of underground natural gas storage, the SOC710VP (spectral range: 400～1 000 nm) is used to obtain the hyperspectral images of winter wheat in the control and stress areas on the 11th, 24th, 32nd, 40th and 49th days accordingly. After preprocessing steps such as spectral smoothing, reflectivity correction and clipping, (1) ANOVA (Analysis of Variation) is first used to select the following characteristic bands: 510, 520, 570, 625, 645, 680 and 690 nm, respectively. Secondly, the gray level co-occurrence matrix (GLCM) is used to calculate the texture features of the characteristic band images and the first three order color moments of RGB synthetic images are calculated. (2) NDVI is used to segment the image, which is divided into vegetation part and bare soil part. Based on least squares support vector machine (LSSVM), a recognition model was constructed to recognize wheat under stress by fusing spectral, texture and color features. (3) The recognition results of wheat and bare soil under stress are fused and processed by morphology and circle fitting. The results showed that there are significant differences in the spectrum, texture and color characteristics between wheat under natural gas stress and wheat without stress. The areas under natural gas stress tend to expand first and then shrink. The recognition model constructed in this paper synthesizes spectral, texture and color features can better identify the stress area on the 24th day of stress occurrence. It is also found that wheat grew more vigorously and appeared “green halo” in the circle 0.25 m from the stress edge. In order to verify the applicability of the model, this paper used the established identification model to identify the soybean, corn and grassland in the experimental area, and achieves satisfying results. The research results can provide theoretical support for the identification of natural gas leakage in engineering applications.

Key words：Hyperspectral; Natural gas micro-leakage; Stress characteristics; Recognition model

Received: 2019-09-07 Accepted: 2020-01-16

ZTFLH:

P237

Corresponding Authors: JIANG Jin-bao E-mail: jjb@cumtb.edu.cn

Cite this article:

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2020)10-3123-07 OR https://www.gpxygpfx.com/EN/Y2020/V40/I10/3123

[1] National Development and Reform Commission（国家发展和改革委委员会）. The 13th Five-Year Plan for Natural Gas Development（《天然气发展“十三五规划”》），2016.
[2] ZHANG Gang-xiong，LI Bin，ZHENG De-wen, et al（张刚雄，李彬，郑得文, 等）. Natural Gas Industry（天然气工业），2017，37(1)：153.
[3] CHEN Xiao-yuan，TAN Yu-fei（陈晓源, 谭羽非）. Oil & Gas Storage and Transportation（油气储运），2011, 30(7)：513.
[4] Lassalle G, Credoz A, Hédacq, Rémy, et al. Environmental Science & Technology, 2018, 52(4): 1756.
[5] Sanches I D, Filho C R S, Magalhães L A, et al. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 78(4): 85.
[6] Smith K L, Steven M D, Colls J J. Remote Sensing of Environment, 2004, 92(2): 207.
[7] Noomen M F, Werff H M A V D, Meer F D V D. Ecological Informatics, 2012, 8(2): 55.
[8] Werff H M A, Bakker W H, van der Meer F D, et al. Computers & Geosciences, 2006, 32(9): 1334.
[9] Jiang J, Qiao X, He R. Journal of Food Engineering, 2016, 169: 284.
[10] Qiao X, Jiang J, Qi X, et al. Food Chemistry, 2017, 220: 393.
[11] GAO Yun-liang, MA Ling(高运良，马玲). Mathematical Statistics(数理统计). Beijing: China Coal Industry Publishing House(北京：煤炭工业出版社)，2002.
[12] ZHANG Shuai-tang, WANG Zi-yan, ZOU Xiu-guo, et al(张帅堂, 王紫烟, 邹修国, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2017，33(22)：200.
[13] HAN Ding, WU Pei, ZHANG Qiang, et al(韩丁，武佩，张强，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(23): 168.
[14] SHEN Qian, ZHU Chang-ming, ZHANG Xin(沈谦, 朱长明, 张新). Bulletin of Surveying and Mapping(测绘通报), 2019，(7): 33.
[15] ZHAO Chun-hui, WANG Li-guo, QI Bin(赵春晖，王立国，齐滨). Hyperspectral Remote Sensing Image Processing Method and Application (高光谱遥感图像处理方法及应用). Beijing: Publishing House of Electronics Industry(北京：电子工业出版社)，2016. 187.