| D-S证据理论融合多特征的油菜病害识别方法 |
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| 引用本文： | 卜翔宇,沈明玉,胡敏,许良凤,徐小兵. D-S证据理论融合多特征的油菜病害识别方法[J]. 电子测量与仪器学报, 2017, 31(1). DOI: 10.13382/j.jemi.2017.01.009 |
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| 作者姓名： | 卜翔宇 沈明玉 胡敏 许良凤 徐小兵 |
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| 作者单位： | 1. 合肥工业大学计算机与信息学院 合肥230009;合肥工业大学情感计算与先进智能机器安徽省重点实验室 合肥230009;2. 合肥工业大学计算机与信息学院 合肥230009 |
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| 基金项目： | 安徽省自然科学基金，国家自然科学基金，国家自然科学青年基金 |
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| 摘 要： | 针对单一特征在识别油菜病害上存在的局限性,提出一种基于D-S证据理论融合多特征的油菜病害识别方法。首先对预处理后的油菜图片提取颜色矩、颜色共生矩阵两种特征,通过欧氏距离来构建D-S证据理论所必需的基本概率分配(BPA),最后运用D-S证据组合规则进行决策级融合,依据决策条件输出最终分类识别结果。针对存在最终识别结果被误识别为不确定问题,通过引入方差来对决策方法进行改进,避免了这一现象的产生。利用该方法在采集到的油菜样本上进行实验,取得了97.09%的识别率。实验表明,该方法能有效提高油菜病害识别率。
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| 关 键 词： | 多特征 欧氏距离 D-S证据理论 方差 |
Rape disease recognition method based on multi-feature and D-S evidence theory |
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| Bu Xiangyu,Shen Mingyu,Hu Min,Xu Liangfeng,Xu Xiaobing. Rape disease recognition method based on multi-feature and D-S evidence theory[J]. Journal of Electronic Measurement and Instrument, 2017, 31(1). DOI: 10.13382/j.jemi.2017.01.009 |
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| Authors: | Bu Xiangyu Shen Mingyu Hu Min Xu Liangfeng Xu Xiaobing |
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| Abstract: | In order to overcome the limitation of single feature in crop disease recognition,this paper presents a method of recognizing rape disease based on D-S evidence theory and multi-feature fusion.Firstly,color matrix and color co-occurrence matrix are extracted as color feature and texture feature from the rape leaves after a series of image processing.Then,with the help of Euclidean distance,the basic probability assignment (BPA) which is necessary for D-S evidence theory can be constructed.Finally,using D-S combination rule of evidence to achieve the decision fusion and outputting the final recognition results through the decision-making conditions.In view of the situation that the final recognition resuh may be misrecognized as uncertain,this paper improves the decision-making method by introducing the variance,which can avoid this defect.The experiment on the collected rape images obtains the recognition rate of 97.09％.The experiments show that the method proposed in this paper can increase the rape disease recognition rate effectively. |
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| Keywords: | multi-feature Euclidean distance D-S evidence theory variance |
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