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摘要： 为及时准确地监测柑橘种植信息，以江西省会昌县作为研究区，采用EO\|1 Hyperion高光谱影像作为数据源，构建了基于混合像元分解的高光谱影像柑橘识别方法。首先，针对EO\|1 Hyperion高光谱影像提供了242个波段，光谱范围广的特点，在波段选择、大气校正等预处理的基础上，提取研究区典型地物端元光谱曲线；然后，利用全约束线性光谱混合模型进行混合像元分解，提取出柑橘端元的丰度值，并通过对照高分遥感影像，构建柑橘端元丰度与柑橘实际种植的对应的关系。结果表明：由于典型地物端元提取中不可避免的误差及柑橘冠层覆盖度的差异，柑橘种植的准确识别与其柑橘端元丰度阈值存在对应关系。在经过反复试验的条件下，研究区柑橘端元丰度阈值设定在0.30~0.45范围之内，总精度达到90%以上，能够满足柑橘种植识别要求。 关键词： 柑橘识别;  EO-1 Hyperion高光谱影像;  混合像元分解;  ')" href="#">柑橘丰度值     Abstract: In order to monitor the citrus planting information timely and accurately，We take Huichang County of Jiangxi Province as the research area，using EO\|1 Hypersion hyperspectral remote sensing (HRS)image as a datasource to build a citrus recognition methods of hyperspectral remote sensing image based on spectral unmixing.First of all，the EO\|1 Hyperion hyperspectral remote sensing image has 242 bands，and it has a wide spectrum rang.It can extract the spectral curve of typical objects in the study area，which is based on the image pre\|processing including the band selection，the atmospheric correction and so on.Then，we use the fully constrained linear spectral mixture model of spectral unmixing to decompose the mixed pixels of the image，and then extract the abundance value of citrus.Finally，we construct the relationship between citrus abundance and the actual cultivation of citrus based on the high resolution remote sensing image.The results indicated that the unavoidable error in the extraction of the typical objects and the differences of the citrus canopy coverage can lead to the corresponding relationship between the citrus plant accurate identification and the citrus abundance threshold value.Under the condition of repeated experiments，the study area of citrus abundance thresholds in the range of 0.30~0.45，the overall accuracy can reach more than 90%，and it can meet the requirements of identification of citrus.

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