【摘要】 针对单一特征在场景分类中精度不高的问题,借鉴信息融合的思想,提出了一种兼顾特征级融合和决策级融合的分类方法。首先,提取图像的尺度不变特征变换词包(SIFT-Bo W)、Gist、局部二值模式(LBP)、Laws纹理以及颜色直方图五种特征。然后,将每种特征单独对场景进行分类得到的结果以Dezert-Smarandache理论(DSm T)推理的方式在决策级进行融合,获得决策级融合下的分类结果;同时,将五种特征串行连接实现特征级融合并进行分类,得到特征级融合下的分类结果。最后,将特征级和决策级的分类结果进行自适应的再次融合完成场景分类。在决策级融合中,为解决DSm T推理过程中基本信度赋值(BBA)构造困难的问题,提出一种利用训练样本构造后验概率矩阵来完成基本信度赋值的方法。在21类遥感数据集上进行分类实验,当训练样本和测试样本各为50幅时,分类精度达到88.61%,较单一特征中的最高精度提升了12.27个百分点,同时也高于单独进行串行连接的特征级融合或DSm T推理的决策级融合的分类精度。更多还原

【Abstract】 Since the accuracy of single feature in scene classification is low, inspired by information fusion, a classification method combined feature-level and decision-level fusion was proposed. Firstly, Scale Invariant Feature Transform-Bag of Words( SIFT-Bo W),Gist,Local Binary Patterns( LBP),Laws texture and color histogram features of image were extracted. Then,the classification results of every single feature were fused in the way of Dezert-Smarandache Theory( DSm T) to obtain the decision-level fusion result; at the same time,the five features were serially connected to generate a new feature,the new feature was used to classification to obtain the feature-level fusion result. Finally,the featurelevel and decision-level fusion results were adaptively fused to finish classification. To solve the Basic Belief Assignment( BBA) problem of DSm T,a method based on posterior probability matrix was proposed. The accuracy of the proposed method on 21 classes of remote sensing images is 88. 61% when training and testing samples are both 50,which is 12. 27 percentage points higher than the highest accuracy of single feature. The accuracy of proposed method is also higher than that of the feature-level fusion serial connection or DSm T reasoning decision-level fusion.更多还原