开集分类识别是近10多年来模式识别领域研究的热点,它能够识别训练集中已知类别的测试样本,同时还能够有效“拒识”未知类别的测试样本;这些未知类别样本不包含在训练集中。现有的开集分类识别算法主要是基于Support Vector Machine(SVM...开集分类识别是近10多年来模式识别领域研究的热点,它能够识别训练集中已知类别的测试样本,同时还能够有效“拒识”未知类别的测试样本;这些未知类别样本不包含在训练集中。现有的开集分类识别算法主要是基于Support Vector Machine(SVM)和深度学习网络框架进行改进,并且主要应用在自然景物图像领域中;在光谱分析领域中还鲜有报道。将传统的闭集框架下的模糊推理分类器进行模型改进,提出了开集框架下的改进模糊推理分类器,并将其应用到木材树种近红外光谱分类识别中。首先,使用Flame-NIR近红外微型光谱仪采集木材样本横切面的近红外光谱曲线,采用Metric Learning算法进行光谱向量维度约简降维至4维(4D)。其次,改进闭集框架下的模糊推理分类器,根据模糊规则置信度和各维度隶属度概率的乘积构建Generalized Basic Probability Assignment(GBPA),再根据GBPA进行分类处理。在20个树种的具有不同的Openness指标下的近红外光谱数据集的分类识别对比实验表明,改进的开集模糊推理分类器(fuzzy reasoning classifier in an open set,FRCOS)优于现有的基于机器学习和深度学习的开集分类识别主流算法,具有较好的评价指标F-Score,Kappa系数及总体识别率。展开更多
Land cover classification is the core of converting satellite imagery to available geographic data.However,spectral signatures do not always provide enough information in classification decisions.Thus,the application ...Land cover classification is the core of converting satellite imagery to available geographic data.However,spectral signatures do not always provide enough information in classification decisions.Thus,the application of multi-source data becomes necessary.This paper presents an evidential reasoning (ER) approach to incorporate Landsat TM imagery,altitude and slope data.Results show that multi-source data contribute to the classification accuracy achieved by the ER method,whereas play a negative role to that derived by maximum likelihood classifier (MLC).In comparison to the results derived based on TM imagery alone,the overall accuracy rate of the ER method increases by 7.66% and that of the MLC method decreases by 8.35% when all data sources (TM plus altitude and slope) are accessible.The ER method is regarded as a better approach for multi-source image classification.In addition,the method produces not only an accurate classification result,but also the uncertainty which presents the inherent difficulty in classification decisions.The uncertainty associated to the ER classification image is evaluated and proved to be useful for improved classification accuracy.展开更多
论文介绍了系统因素结构反分析子框架(inward analysis of system factor structure,IASFS).它是为了解系统在工作环境因素变化过程中系统可靠性的变化.论文根据其对应关系进行逻辑推理得到规则,进而形成系统等效响应结构.得到适合系统...论文介绍了系统因素结构反分析子框架(inward analysis of system factor structure,IASFS).它是为了解系统在工作环境因素变化过程中系统可靠性的变化.论文根据其对应关系进行逻辑推理得到规则,进而形成系统等效响应结构.得到适合系统工作的因素状态组合.IASFS是笔者提出的SFT框架的一部分.根据IASFS的特点定义了01型空间故障树,其结构化表示法为图法和表法.基于表法的IASFS方法有逐条分析法和分类推理法.论文主要定义和描述了逐条分析法和分类推理法.说明了IASFS是一个系统结构反分析人机认知体.推理结果表明,就系统与因素的状态关系而言,系统结构为T=A_1·A_4+A_3·A_5+A_1·A_2·A_3.逐条分析法和分类推理法可得到等效响应结构,其与被分析系统对工作环境因素变化响应相同.展开更多
文摘开集分类识别是近10多年来模式识别领域研究的热点,它能够识别训练集中已知类别的测试样本,同时还能够有效“拒识”未知类别的测试样本;这些未知类别样本不包含在训练集中。现有的开集分类识别算法主要是基于Support Vector Machine(SVM)和深度学习网络框架进行改进,并且主要应用在自然景物图像领域中;在光谱分析领域中还鲜有报道。将传统的闭集框架下的模糊推理分类器进行模型改进,提出了开集框架下的改进模糊推理分类器,并将其应用到木材树种近红外光谱分类识别中。首先,使用Flame-NIR近红外微型光谱仪采集木材样本横切面的近红外光谱曲线,采用Metric Learning算法进行光谱向量维度约简降维至4维(4D)。其次,改进闭集框架下的模糊推理分类器,根据模糊规则置信度和各维度隶属度概率的乘积构建Generalized Basic Probability Assignment(GBPA),再根据GBPA进行分类处理。在20个树种的具有不同的Openness指标下的近红外光谱数据集的分类识别对比实验表明,改进的开集模糊推理分类器(fuzzy reasoning classifier in an open set,FRCOS)优于现有的基于机器学习和深度学习的开集分类识别主流算法,具有较好的评价指标F-Score,Kappa系数及总体识别率。
基金Under the auspices of National Natural Science Foundation of China (No.40871188)Knowledge Innovation Programs of Chinese Academy of Sciences (No.INFO-115-C01-SDB4-05)
文摘Land cover classification is the core of converting satellite imagery to available geographic data.However,spectral signatures do not always provide enough information in classification decisions.Thus,the application of multi-source data becomes necessary.This paper presents an evidential reasoning (ER) approach to incorporate Landsat TM imagery,altitude and slope data.Results show that multi-source data contribute to the classification accuracy achieved by the ER method,whereas play a negative role to that derived by maximum likelihood classifier (MLC).In comparison to the results derived based on TM imagery alone,the overall accuracy rate of the ER method increases by 7.66% and that of the MLC method decreases by 8.35% when all data sources (TM plus altitude and slope) are accessible.The ER method is regarded as a better approach for multi-source image classification.In addition,the method produces not only an accurate classification result,but also the uncertainty which presents the inherent difficulty in classification decisions.The uncertainty associated to the ER classification image is evaluated and proved to be useful for improved classification accuracy.
文摘论文介绍了系统因素结构反分析子框架(inward analysis of system factor structure,IASFS).它是为了解系统在工作环境因素变化过程中系统可靠性的变化.论文根据其对应关系进行逻辑推理得到规则,进而形成系统等效响应结构.得到适合系统工作的因素状态组合.IASFS是笔者提出的SFT框架的一部分.根据IASFS的特点定义了01型空间故障树,其结构化表示法为图法和表法.基于表法的IASFS方法有逐条分析法和分类推理法.论文主要定义和描述了逐条分析法和分类推理法.说明了IASFS是一个系统结构反分析人机认知体.推理结果表明,就系统与因素的状态关系而言,系统结构为T=A_1·A_4+A_3·A_5+A_1·A_2·A_3.逐条分析法和分类推理法可得到等效响应结构,其与被分析系统对工作环境因素变化响应相同.
