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首页> 外文期刊>International journal of remote sensing >Development of a crop-specific spectral library and discrimination of various agricultural crop varieties using hyperspectral imagery
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Development of a crop-specific spectral library and discrimination of various agricultural crop varieties using hyperspectral imagery

机译：开发特定作物的光谱库，并使用高光谱图像判别各种农作物品种

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摘要

In the context of a growing interest in remote sensing for precision agriculture applications, the utility of space-borne hyperspectral imaging for the development of crop-specific spectral library and automatic identification and classification of three important varieties for each of rice, chillies, sugar cane, and cotton crops in Andhra Pradesh state of India has been investigated in this study. The classification of crops at variety level using two spectral libraries developed using hyperspectral reflectance data at canopy scale (in situ hyperspectral measurements) and at pixel scale (Hyperion data) has shown promising results, with 86.5% and 88.8% overall classification accuracy, respectively. This observation highlights the possible integration of in situ hyperspectral measurements with space-borne hyperspectral remote-sensing data for automatic identification and discrimination of various crop varieties. However, considerable spectral similarity is observed between varieties of rice and sugar-cane crops. This spectral similarity, if common to most of the crop species, may pose problems for the accurate discrimination of various crop varieties using spectral measurements.
机译：在对精密农业应用遥感的兴趣日益增长的背景下，利用星载高光谱成像技术开发作物专用光谱库并自动识别和分类稻米，辣椒，甘蔗中的三个重要品种这项研究已经对印度安得拉邦的棉花作物进行了调查。使用两个光谱库对作物品种进行分类，这两个光谱库是利用冠层尺度（原位高光谱测量）和象素尺度（Hyperion数据）开发的高光谱反射率数据开发的，具有良好的分类效果，总体分类精度分别为86.5％和88.8％。该观察结果强调了将原位高光谱测量结果与星载高光谱遥感数据整合在一起以自动识别和区分各种作物品种的可能性。但是，在水稻和甘蔗作物品种之间观察到相当大的光谱相似性。如果大多数作物种类都具有这种光谱相似性，则可能会给使用光谱测量准确区分各种作物品种带来麻烦。

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