首页|基于高光谱影像的小麦条锈病光谱信息探测与提取

基于高光谱影像的小麦条锈病光谱信息探测与提取

Spectral Information Detection and Extraction of Wheat Stripe Rust Based on Hyperspectral Image

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为了理解与定量分析农作物光谱特征和相关参数,基于PHI高光谱影像建立了农作物病害光谱响应与探测模型,即光谱点位与参数模型(FPPM).为了识别和提取小麦条锈病信息,根据多时相高光谱影像光谱特征,提出了一种可调节的多时相归一化植被指数(MT-NDVI).结果表明,FPPM能很好地响应与感知该病害在小麦生长期的光谱特征;结合光谱角制图法(SAM),MT-NDVI能清楚地呈现不同区域该病害的轻重程度,准确地区分和提取小麦条锈病与健康小麦及土壤的信息.
In order to analyze and detect wheat stripe rust disease quantitatively, a Feature Position and Parameter Mode (FPPM) of spectral responses and disease detection is built based on the airborne wheat PHI(Pushbroom hyperspectral imager) images, and a new adjustable Multi-Temporal Normalized Difference Vegetation Index (MT-NDVI) is provided to extract the disease information in terms of the temporal spectral features. The results show that the FPPM can well response and monitor the disease occurring during the wheat growing, and the MT-NDVI method integrating Spectral Angle Mapper(SAM) technique can represent clearly the disease where is serious, slight or not influential, distinguish and extract the disease information accurately from health wheat and soil.

Hyperspectral imageWheat stripe rustSpectral responsesInformation extraction

杨可明、陈云浩、郭达志、蒋金豹

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中国矿业大学(北京)测绘与土地科学系,北京,100083

北京师范大学资源学院,北京,100875

高光谱影像 小麦条锈病 光谱响应 信息提取

Civilian Special Scientific Research Program of China Industry of National Defence Science and TechnologyNational Foundation Surveying Program of Key Laboratory of Geo-Informatics of State Bureau of Surveying and Mapping of China

JZ2005001-06

2008

光子学报
中国光学学会 中国科学院西安光学精密机械研究所

光子学报

CSTPCDCSCD北大核心
影响因子：0.948
ISSN：1004-4213
年,卷(期)：2008.37(1)
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