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HYPERSPECTRAL IMAGE FOR DISCRIMINATING APHID AND APHID DAMAGE REGION OF WINTER WHEAT LEAF

文献类型： 外文期刊

作者： Luo Juhua 1 ; Huang Wenjiang 2 ; Guan Qingsong 2 ; Zhao Jinling 3 ; Zhang Jingcheng 3 ;

作者机构： 1.Chinese Acad Sci, Nanjing Inst Geog & Limnol, State Key Lab Lake Sci & Environm, Nanjing, Jiangsu, Peoples R China

2.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Beijing Rse Ctr Informat Technol Agr, Beijing, Peoples R China

关键词： Hyperspectral imaging;Aphid;Leaf;Spectral index;Principal component analysis (PCA)

期刊名称：2013 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)

ISSN： 2153-6996

年卷期： 2013 年

页码：

收录情况： SCI

摘要： Wheat aphid, Sitobion avenae F. is the most destructive insect infesting winter wheat and appears almost annually in northwest China. Past studies have demonstrated the potential of remote sensing for detecting diseases and insects damage. In the study, hyperspectral imaging in the visible and near-infrared (500-900nm) region was tried to determinate aphid of wheat leaf and detect damage region of winter leaf caused by aphid. The principal component analysis (PCA) and spectral indices which used to monitor some stresses were applied to extract aphid information. The result showed that the classification result was better based on the second principal component (PC2) image and the third principal component (PC3) image by principal component (PC) transformation than spectral indices. Then, the mean reflectance of pixels with aphid and pixels without aphid was obtained, respectively, and the most sensitive reflectance regions to aphid were selected in visible and near-infrared by comparing the reflectance difference of two classes. Further, Leaf aphid damage index (LADI) was established according to two the sensitive reflectance region, and the leaf region with aphid, the infested leaf region and healthy leaf region were classified by LADI value of image. The result showed that the aphid damage area ratio of each wheat leaf estimated by pixels number of three classes was consistent with the survey the damage area ratio. So LADI had potential for detecting the leaf damage region caused by aphid.

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