文献类型: 会议论文
第一作者: L. Xia
作者: L. Xia 1 ; R. R. Zhang 1 ; L. P. Chen 1 ; F. Zhao 2 ; H. J. Jiang 1 ;
作者机构: 1.Beijing Research Center of Intelligent Equipment for Agriculture, Beijing Academy of agriculture and forestry Sciences
2.Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, University of Chinese academy of Science
关键词: UAV;Mosaic;Hyperspectral;Images
会议名称: IFAC Conference on Sensing, Control and Automation Technologies for Agriculture
主办单位:
页码: 1-4
摘要: This study employed the feature band pre-selection and composite process to overcome the mosaic difficulties of large number of bands and huge data volume of the hyperspectral UAV images before the mosaic process being done. The application and comparison were done by using the UHD 185 hyperspectral camera, and the result showed that the band pre-selection and composite method used in this study can go up as high as being 390 percent faster than the single band mosaic (for the Normalized Differential Vegetation Index application), and about 46 times faster than full band mosaic. Besides, compared to the full band mosaic method, the method used in the study can reduce the maximum memory usage amount and final mosaic file size significantly.
分类号: TP2-53
- 相关文献
[1]Stitching of hyper-spectral UAV images based on feature bands selection. Xia, L.,Zhang, R. R.,Chen, L. P.,Jiang, H. J.,Xia, L.,Zhang, R. R.,Chen, L. P.,Jiang, H. J.,Xia, L.,Zhang, R. R.,Chen, L. P.,Jiang, H. J.,Xia, L.,Zhang, R. R.,Chen, L. P.,Jiang, H. J.,Zhao, F.. 2016
[2]利用无人机多光谱成像监测油茶碳储量. 陈龙跃,段丹丹,张祖铭,孙鹤,高佳华,姜毅,冉成. 2024
[3]Droplets movement and deposition of an eight-rotor agricultural UAV in downwash flow field. Tang Qing,Zhang Ruirui,Chen Liping,Xu Min,Yi Tongchuan,Zhang Bin,Tang Qing,Zhang Ruirui,Chen Liping,Xu Min,Yi Tongchuan,Zhang Bin,Tang Qing,Zhang Ruirui,Chen Liping,Xu Min,Yi Tongchuan,Zhang Bin. 2017
[4]The DOM Generation and Precise Radiometric Calibration of a UAV-Mounted Miniature Snapshot Hyperspectral Imager. Yang, Guijun,Wang, Yanjie,Feng, Haikuan,Xu, Bo,Yang, Xiaodong,Yang, Guijun,Feng, Haikuan,Xu, Bo,Li, Changchun,Wang, Yanjie,Yuan, Huanhuan. 2017
[5]Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives. Yang, Guijun,Liu, Jiangang,Zhao, Chunjiang,Yu, Haiyang,Xu, Bo,Yang, Xiaodong,Feng, Haikuan,Zhao, Xiaoqing,Li, Zhenhai,Li, Heli,Yang, Hao,Yang, Guijun,Zhao, Chunjiang,Yang, Xiaodong,Li, Zhenhai,Li, Heli,Yang, Hao,Yang, Guijun,Liu, Jiangang,Zhao, Chunjiang,Yu, Haiyang,Xu, Bo,Li, Zhenhong,Huang, Yanbo,Zhu, Dongmei,Zhang, Xiaoyan,Zhang, Ruyang. 2017
[6]Comparative analysis of three regression methods for the winter wheat biomass estimation using hyperspectral measurements. Xingang Xu,Yuanyuan Fu,Guijun Yang,Haikuan Feng,Xiaoyu Song,Jihua Wang. 2013
[7]Estimating Winter Wheat Leaf Area Index From Ground and Hyperspectral Observations Using Vegetation Indices. Xie, Qiaoyun,Huang, Wenjiang,Zhang, Bing,Dong, Yingying,Xie, Qiaoyun,Chen, Pengfei,Song, Xiaoyu,Pascucci, Simone,Pignatti, Stefano,Laneve, Giovanni. 2016
[8]Comparative Study on Remote Sensing Invertion Methods for Estimating Winter Wheat Leaf Area Index. Xie Qiao-yun,Huang Wen-jiang,Peng Dai-liang,Zhang Qing,Xie Qiao-yun,Liang Dong,Huang Lin-sheng,Zhang Dong-yan,Cai Shu-hong,Yang Gui-jun. 2014
[9]MONITORING AVAILABLE PHOSPHORUS CONTENT IN SOIL OF CULTIVATED LAND BASED ON HYPERSPECTRAL TECHNOLOGY. Gu, Xiaohe,Wang, Lei,Wang, Lizhi,Fan, Youbo,Yang, Hao,Long, Huiling. 2016
[10]Monitoring Freeze Stress Levels on Winter Wheat from Hyperspectral Reflectance Data Using Principal Component Analysis. Wang Hui-fang,Huo Zhi-guo,Wang Hui-fang,Wang Ji-hua,Dong Ying-ying,Gu Xiao-he. 2014
[11]A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy. Yue, Jibo,Feng, Haikuan,Yang, Guijun,Li, Zhenhai,Yue, Jibo,Yue, Jibo,Yang, Guijun,Li, Zhenhai,Feng, Haikuan,Yang, Guijun,Li, Zhenhai. 2018
[12]The inversion model of soil organic matter of cultivated land based on hyperspectral technology. Gu, Xiaohe,Wang, Yancang,Song, Xiaoyu,Xu, Xingang. 2015
[13]Differentiation of Yellow Rust and Powdery Mildew in Winter Wheat and Retrieving of Disease Severity Based on Leaf Level Spectral Analysis. Yuan Lin,Zhang Jing-cheng,Zhao Jin-ling,Wang Ji-hua,Yuan Lin,Zhang Jing-cheng,Wang Ji-hua,Huang Wen-jiang. 2013
[14]Research on Universality of Least Squares Support Vector Machine Method for Estimating Leaf Area Index of Winter Wheat. Xie Qiao-yun,Huang Wen-jiang,Peng Dai-liang,Xie Qiao-yun,Liang Dong,Huang Lin-sheng,Zhang Dong-yan,Xie Qiao-yun,Liang Dong,Huang Lin-sheng,Zhang Dong-yan,Song Xiao-yu,Yang Gui-jun. 2014
[15]Using in-situ hyperspectral data for detecting and discriminating yellow rust disease from nutrient stresses. Zhang, Jingcheng,Huang, Wenjiang,Yuan, Lin,Luo, Juhua,Wang, Jihua,Zhang, Jingcheng,Pu, Ruiliang,Zhang, Jingcheng,Yuan, Lin,Wang, Jihua,Huang, Wenjiang. 2012
[16]Monitoring total nitrogen content in soil of cultivated land based on hyperspectral technology. Gu, Xiaohe,Wang, Lizhi,Zhang, Liyan,Yang, Guijun. 2017
[17]Analysis of spectral difference between the foreside and backside of leaves in yellow rust disease detection for winter wheat. Yuan, Lin,Zhang, Jing-Cheng,Wang, Ke,Wang, Ji-Hua,Yuan, Lin,Zhang, Jing-Cheng,Wang, Ji-Hua,Zhao, Jin-Ling,Loraamm, Rebecca-W.,Huang, Wen-Jiang.
[18]Assimilation of Two Variables Derived from Hyperspectral Data into the DSSAT-CERES Model for Grain Yield and Quality Estimation. Li, Zhenhai,Xu, Xingang,Zhao, Chunjiang,Yang, Guijun,Feng, Haikuan,Li, Zhenhai,Xu, Xingang,Zhao, Chunjiang,Yang, Guijun,Feng, Haikuan,Li, Zhenhai,Wang, Jihua,Wang, Jihua,Xu, Xingang,Zhao, Chunjiang,Yang, Guijun,Feng, Haikuan,Xu, Xingang,Zhao, Chunjiang,Yang, Guijun,Feng, Haikuan,Jin, Xiuliang. 2015
[19]Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects. Yuan, Lin,Nie, Chenwei,Wang, Jihua,Zhang, Jingcheng,Yuan, Lin,Nie, Chenwei,Wang, Jihua,Zhang, Jingcheng,Huang, Yanbo,Loraamm, Rebecca W.. 2014
[20]Estimating wheat yield and quality by coupling the DSSAT-CERES model and proximal remote sensing. Li, Zhenhai,Jin, Xiuliang,Zhao, Chunjiang,Xu, Xingang,Yang, Guijun,Li, Cunjun,Shen, Jiaxiao,Li, Zhenhai,Jin, Xiuliang,Zhao, Chunjiang,Xu, Xingang,Yang, Guijun,Li, Cunjun,Shen, Jiaxiao,Zhao, Chunjiang,Zhao, Chunjiang,Li, Zhenhai,Wang, Jihua,Wang, Jihua,Shen, Jiaxiao.


