文献类型： 外文期刊
作者： Liu, Chang 1 ; Yang, Guijun 2 ; Li, Zhenhai 2 ; Tang, Fuquan 1 ; Feng, Haikuan 2 ; Wang, Jianwen 2 ; Zhang, Chunlan 1 ; Zhang, Liyan 2 ;
作者机构： 1.Xian Univ Sci & Technol, Coll Geomat, Xian, Peoples R China
2.Minist Agr PR China, Key Lab Quantitat Remote Sensing Agr, Beijing Res Ctr Informat Technol Agr, Beijing, Peoples R China
3.Natl Engn Res Ctr Informat Technol Agr, Beijing, Peoples R China
4.Beijing Engn Res Ctr Agr Internet Things, Beijing, Peoples R China
关键词： Hyperspectral image; Texture feature; Biomass; Principal component
期刊名称：COMPUTER AND COMPUTING TECHNOLOGIES IN AGRICULTURE XI, CCTA 2017, PT II
ISSN： 1868-4238
年卷期： 2019 年 546 卷
页码：
收录情况： SCI
摘要： Biomass is an important indicator to evaluate vegetation life activities and hyperspectral imagery from unmanned aerial vehicle (UAV) supplied with abundant texture features shows a great potential to estimate crop biomass. In this paper, principal component analysis (PCA) was used to select the principal component bands from UAV hyperspectral image. Eight texture features from the principal component bands were extracted by Gray Level Cooccurrence Matrix method, and the sensitive texture features were finally selected to construct the biomass estimation model. The results show that: (1) Texture features mean, ent, sm, hom, con, dis of the first principal component (pcal) and the mean of the third principal component (pca3) were significantly correlated with the biomass. (2) The biomass model by multiple texture features (R-2 = 0.654, RMSE = 0.808 (10(3) kg/hm(2))) demonstrated better fitting effect than that by single texture feature (R-2 = 0.534, RMSE = 0.960 (10(3) kg/hm(2))). The biomass estimation model based on the texture features of multiple principal components had a good fitting effect. Therefore, texture features of the UAV platform can accurately predict the winter wheat biomass.
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