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分类精确性指数Entropy在潜剖面分析中的表现:一项蒙特卡罗模拟研究 被引量:102
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作者 王孟成 邓俏文 +2 位作者 毕向阳 叶浩生 杨文登 《心理学报》 CSSCI CSCD 北大核心 2017年第11期1473-1482,共10页
本研究通过蒙特卡洛模拟考查了分类精确性指数Entropy及其变式受样本量、潜类别数目、类别距离和指标个数及其组合的影响情况。研究结果表明:(1)尽管Entropy值与分类精确性高相关,但其值随类别数、样本量和指标数的变化而变化,很难确定... 本研究通过蒙特卡洛模拟考查了分类精确性指数Entropy及其变式受样本量、潜类别数目、类别距离和指标个数及其组合的影响情况。研究结果表明:(1)尽管Entropy值与分类精确性高相关,但其值随类别数、样本量和指标数的变化而变化,很难确定唯一的临界值;(2)其他条件不变的情况下,样本量越大,Entropy的值越小,分类精确性越差;(3)类别距离对分类精确性的影响具有跨样本量和跨类别数的一致性;(4)小样本(N=50~100)的情况下,指标数越多,Entropy的结果越好;(5)在各种条件下Entropy对分类错误率比其它变式更灵敏。 展开更多
关键词 潜剖面分析 分类精确性 ENTROPY 潜类别距离 蒙特卡洛模拟
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Land Cover Classification with Multi-source Data Using Evidential Reasoning Approach 被引量:3
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作者 LI Huapeng ZHANG Shuqing +1 位作者 SUN Yan GAO Jing 《Chinese Geographical Science》 SCIE CSCD 2011年第3期312-321,共10页
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
关键词 evidential reasoning Dempster-Shafer theory of evidence multi-source data geographic ancillary data land cover classification classification uncertainty
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