基于证据理论组合多分类规则实现大区域植被遥感分类研究

发布时间：2018-03-29 22:34

  本文选题：遥感　切入点：大区域　出处：《林业科学研究》2017年02期

【摘要】：[目的]利用遥感影像的时效性和宏观性特点,基于证据理论组合多分类规则的方法快速和高效地实现大区域植被遥感分类。[方法]首先,依据辨识框架的概念设计分类系统,并采用大区域样本快速采集方法提取训练样点;其次,将多个单分类规则得到的植被类型特征影像归一化处理为基本概率赋值作为表达对各类型信任程度的证据源数据,再将不同证据源的信任度信息依据证据理论组合;再次,将组合结果依据最大信任度原则确定植被类型;最后,在中国植被图与中国土地覆盖图的类型一致区域随机布点作为验证样本。[结果]各单分类器分类结果的总体精度范围为60%70%,两两规则组合分类结果的总体精度范围为70%80%,3个规则组合分类结果的总体精度达到80.84%。[结论]组合多分类规则的证据理论分类方法可以提高分类精度;参与组合的单分类器精度越高,相关证据源越多,组合分类结果精度越高。
[Abstract]:[objective] based on the characteristics of timeliness and macroscopicity of remote sensing images, the classification system of vegetation in large area is designed quickly and efficiently based on evidence theory and multi-classification rules. [methods] first of all, according to the concept of identification framework, a classification system is designed. The training points are extracted by using the fast sampling method of large area samples. Secondly, the normalized vegetation type image obtained by multiple single classification rules is treated as the basic probability assignment as the evidence source data to express the degree of trust in each type. Then, the trust information of different evidence sources is combined according to the evidence theory; thirdly, the combination results are determined according to the principle of maximum trust. Finally, Random distribution of points in the area consistent with the type of vegetation map and land cover map in China is used as the verification sample. [results] the overall accuracy range of the classification results of each single classifier is 60 pand, and the total precision range of the results of the combination of pairwise rules is 60 p. The overall accuracy of the three rule combination classification results is 80.844.Conclusion the classification accuracy can be improved by combining the evidence theory classification method with multiple classification rules. The higher the accuracy of the combined single classifier, the more relevant evidence sources, the higher the accuracy of the combined classification results.
【作者单位】： 中国林业科学研究院资源信息研究所;
【基金】：国家863计划课题(2012AA102001)
【分类号】：Q948;TP79
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本文编号：1683189