%0 Journal Article
%T 多特征的光学遥感图像多目标识别算法
%A 姬晓飞
%A 秦宁丽
%A 刘洋
%J 智能系统学报
%D 2016
%R 10.11992/tis.201511011
%X 基于单一特征的光学遥感图像多目标分类识别存在准确性较差的问题，提出一种新的基于多特征决策级融合的多目标分类识别算法。首先对光学遥感图像目标提取3种能够同时满足平移、旋转和尺度不变性的特征：可以描述局部和全局分布特性的分层BoF-SIFT特征，描述目标边缘轮廓点信息的改进后的SC形状特征，对图像中较大目标识别较好的Hu不变矩特征；其次采用基于径向基核函数的一对一支持向量机算法分别获得3种特征的目标识别概率，并设计了一种多特征决策级加权融合的策略实现对多目标的分类。经多次实验验证该算法对光学遥感图像多目标具有较好的分类识别性能，达到了93.52%的正确识别率。</br>A novel multi-feature decision level fusion recognition algorithm is proposed to solve the problem of poor levels of accuracy with single feature based multi-target classification of optical remote sensing images. Firstly, three kinds of features which can not only meet translation, rotation, and scale invariance are extracted. One is the hierarchical BoF-SIFT feature which can simultaneously describe local and global distributions. Another is the improved shape context feature which is used to describe the target edge contour point information. The other one is Hu moment invariants which gives better levels of recognition performance for large targets. Secondly, the recognition probabilities of these features are obtained using a one versus one support vector machine based on a radial basis function. Thirdly a strategy for multi-feature decision level fusion is designed. A large number of experiments show that the algorithm for multi-target classification of optical remote sensing images performs better with the recognition rate of targets reaching 93.52%
%K 光学遥感图像
%K 多特征的决策级融合
%K 分层的BoF-SIFT特征
%K SC形状特征
%K Hu不变矩特征
%K 支持向量机&lt
%K /br&gt
%K optical remote sensing image
%K multi-features decision level fusion
%K hierarchical BoF-SIFT feature
%K shape context feature
%K Hu moment invariants
%K support vector machine
%U http://tis.hrbeu.edu.cn/oa/darticle.aspx?type=view&id=201511011