Crop indentification based on hyperspectral remote sensing
Abstract
I hyperspectral Remote Sensing provides a new technical means for the identification of crop
species<b 12>, which isof significance for thedevelopment of precision agriculture. In this study, spectral
characteristics-based identification were conducted on 7 crops at harvest by using different data Forms and
commonly used vegetation indices. The reflectivity of Canna is very prominent in 350-500 nm wavelength, and The spectral reflectance of crops varied in 760-915 nm,1 000-1/Nm. The best wavelengths for identification
of the 7 crops is 516 nm, 568 nm, 609 nm, 642nm, </b 18>660 nm,nm, 717 nm , 760 nm , 928 nm , 1 001 nm , 1
118 nm , 1 136 nm and 1 327 nm. Among vegetation indices, rv/ showed the strongest identifying potential
followed by Msri b129>,NV/, tdv/ , ev/ , ndv/ , sav/ , DV /, tv/ , /pv/.To sum up , The characteristic spectrum and
vegetation index are capable of crop discrimination.
References
Remote Sensing | 51. balaguer A, RUIZ L A , hermosill T , et al . Definition To a comprehensive set ofof texture semivariogram features and their eval uation for object-orientied Imageclassification J ] . Computers , Geosciences,2010, 36:231- 2. JIA K, WU B , LI Q. crop classication using HJ satellite multispectral data in North , Plain J. . Journal to applied Remote Sensing, 2013,7!573-576. 3. Tang Bin, Wang Fu Min, Zhou Liuping, etc. . Regional Rice Remote sensing yield estimation based onprefecture- level and Space study J . Jiangsu Agricultural Science , 2015, : 525-528. 4. Owenhao , su , Shiwenzhen , etc . Is based on the HJ 4 three major agricultural work for satellite imagery Objectyield best choice J . Journal of Agricultural Engineering , : 176-1825. Jing Ting, Li Weiguo, King book , etc . image fusion based winter wheat planting area Extract appropriate scalingresearch J]. Jiangsu Agricultural Journal , 2015, To (6) : 1312-1317. 6. li feng, Zhaohong, Zhao Yukin, etc. . Is based on the HJ 4 CCD image of winter wheat species Plant Area extractionstudy ? Shandong Agricultural Science , 2015,47 (5): 109414. 7. Chen Liufeng, Lin Kaiping, Hu Baoqing, etc. . Is based on the Landsat 8_ OLI Sugarcane species for data PlantArea monitoring J . Southern Journal of Agriculture , 2015, : 2068^0728. Weekly Clear . Current situation and development trend of farming remote sensing at home and abroad J ]. ChinaAgricultural Resources vs. Zoning ,, (5) : 944. 9. Zhang Xiwang, Liu Jianfeng, , qinfen , etc . Progress in remote sensing identification of crop types J ]. ChinaAgriculture Bulletin , 2014, : 278485. 10. Wang Yu, Wu see. . Research on hyperspectral Remote sensing identification of crop species J ]. Geographyand Geo-Information science ,2015, 2 : 29-33. 11. xing dongxing , Changqingrui . Identification of tree species based on spectral analysis J ]. Spectrum Learning andspectral analysis , 2009,7 (x) : 1937494012. Li Ruijun , Niekeyan , Peng Zhilao , , and so on . Canopy reflectance spectra of pepper ShengJ . Guizhou Agricultural Sciences , 2014, : 230^33. 13. shen Palm Spring , Wang Ke , The crowd of Kings . dynamic Variation of spectral reflectance characteristics ofrice canopy Research J . Land and resources remote sensing , 1996 (4): 40- T 414. Feng Wei , Guo Tianji , Thank you for welcoming , , and so on . Crop Spectral analysis technology and itsapplication in growth monitoring application in test J . China Agriculture Bulletin , 2009 , : 182488. 15. Huang jingfeng, Wang Fu Min, Wang xiuzhen. . Experimental Study on rice hyperspectral remoteSensing [M[]. Hang State : Zhejiang University Press , 010. 16. Fengmingbo , New . An empirical model based Hyperion Data vegetation chlorophyll contains volumeinversion J . Land and resources remote sensing , 2014 , 1 : 71-77. 17. Wang lei , White to road . progress in crop nutrition diagnosis based on spectral theory J ]. Journal of PlantNutrition and fertilizers , 2006 , 6 : 902~91
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