| 采用多光谱图像融合提高作物和杂草灰度比值 |
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| 引用本文： | 吕俊伟,马成林,于永胜.采用多光谱图像融合提高作物和杂草灰度比值[J].农业工程学报,2005,21(11):99-102. |
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| 作者姓名： | 吕俊伟 马成林 于永胜 |
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| 作者单位： | 海军航空工程学院控制工程系,烟台,264001;吉林大学生物与农业工程学院,长春,130025;山东工商学院数学系,烟台,264005 |
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| 摘 要： | 作物和杂草在图像中的灰度比值对识别率有着重要的影响。提出了一种利用多光谱图像融合的方法提高它们在图像中的灰度比值。为了采用多光谱图像，研制了基于黑白摄像机和多种滤光片的计算机控制的多光谱图像采集系统。在对洋葱(作物)、野芥末草(杂草)和土壤在多光谱图像中灰度比值研究的基础上，对多种多光谱图像融合方式进行了对比试验研究，发现以b+ir-g-r等图像融合方式给出了比较好的结果。把这些图像融合方式应用到图像识别中，其结果表明，多光谱图像融合方法比仅采用彩色分量的融合方法，其识别误差减少了22％。文中同时给出了评价作物、杂草和土壤在图像中灰度比值指标的方法。
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| 关 键 词： | 多光谱图像 图像融合 作物和杂草 灰度比值 |
| 文章编号： | 1002-6819（2005）11-0099-04 |
| 收稿时间： | 3/8/2005 12:00:00 AM |
| 修稿时间： | 8/6/2005 12:00:00 AM |
Multi-spectral image fusion to increase gray level ratio between crop and weeds in image |
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| Lü Junwei,Ma Chenglin,Yu Yongsheng.Multi-spectral image fusion to increase gray level ratio between crop and weeds in image[J].Transactions of the Chinese Society of Agricultural Engineering,2005,21(11):99-102. |
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| Authors: | Lü Junwei Ma Chenglin Yu Yongsheng |
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| Institution: | 1. Department of Control Engineering, Naval Aviation Engineering Institute, Yantai 264001, China; 2. College of Biological and Agricultural Engineering, Jilin University, 130025 ,China; 3. Department of mathematics, Shandong Institute of Business and Technology, Yantai 264005 China |
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| Abstract: | The gray level ratio between crop and weed in image has a great influence on the effect of distinguishing them. A method for utilizing multi-spectral image fusion to increase the gray level ratio was suggested in this paper. By utilizing multi-spectral images, a system for acquiring multi-spectral images was built up with black and white camera and four filters. Based on studying the gray level ratio with onion (crop), mustard (weed) and soil in multi-spectral images, many modes of multi-spectral image fusion were compared and studied through testing results, it was found that some modes of multi-spectral image fusion like b+ir-g-r give better results. Applying these modes to distinguish onion (crop) from mustard (weeds) in images, the results showed that the errors of identifying onion (crop) and mustard (weeds) were decreased by 22%. A method to evaluate the indices of gray level ratios of crop, weed and soil in images was also defined in this paper. |
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| Keywords: | multi-spectral images image fusion crops and weeds gray level ratio |
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