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Rice Blast Area Monitoring Based on HJ-CCD Imagery

机译:基于HJ-CCD图像的稻瘟病监测

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摘要

It is difficult to determine the health of rice for simple vegetation index (NDVI) thresholding method which is widely used through remote sensing technology in crop disaster monitoring. The study selected binary logistic regression which were respectively established vegetation index getting from measured spectral and the relationship model between health status. The results show that the triangular vegetation index TVI model is with better reliability. When remote sensing monitoring rice blast was taken into account, geographical range was widely involved and rice-growing conditions were existing obvious differences in the local area, using a 3 × 3 pixel neighborhood consistency assumption to eliminate differences in the local environment. Applying China's own property "environmental disaster satellite" CCD sensor data into the model and the stress range of extracting rice blast was basically consistent with Plant Protection Institute of Heilongjiang Academy of Agricultural Reclamation Sciences as well as the ground measured results, among which TVI model results accuracy reached 76.47%, which can meet remote sensing monitoring requirements. of the blast area.
机译:难以通过在作物灾害监测中遥感技术广泛使用的简单植被指数(NDVI)阈值化方法难以确定水稻的健康。该研究选择了二进制逻辑回归,分别是从测量的频谱和健康状况之间的关系模型建立了植被指数。结果表明,三角植被指数TVI模型具有更好的可靠性。当考虑到遥感监测稻瘟病时,广泛涉及地理范围,局部地区存在稻米的条件在局域出现明显差异,使用3×3像素邻域一致性假设来消除当地环境的差异。将中国自己的财产“环境灾害卫星”CCD传感器数据进入模型,提取稻瘟病的应力范围与黑龙江农业填海科学院的植物保护研究所以及地面测量结果一致,在其中TVI模型结果精度达到76.47%,可以满足遥感监控要求。爆炸区域。

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