摘要
提出用多阀值分类和属性形态学对月球图像进行层次性和选择性预处理,使高对比阴阳月牙对和低灰度弱边界椭圆形撞击坑具备基本规范和稳定的Haar和PHOG特征。文中探讨了这些预处理方法对局部区域的小波Haar特征和塔式梯度方向直方图PHOG特征的稳定性的影响与作用,然后研究了AdaBoost和SVM这两种分类算法在月球撞击坑探测中的作用,并研究了将Haar和PHOG特征与AdaBoost和SVM相结合对撞击坑候选区域进行精确和高效定位的集成方法。结果表明利用属性形态学的柔性结构滤波和分类方法的集成应用能够使撞击坑的识别率较传统方法提高2%-5%。
A method based on multi-threshold classification and attribute morphology is adopted to preprocess lunar image selectively in different gray layers,which ensure different highlight shadow crescent pair and low gray weak edge elliptical crater possess essentially normalized and steady Haar and PHOG feature.The influence and the function of the preprocessing method on partial wavelet Haar and pyramid histogram of oriented gradients feature is probed,and the effects of AdaBoost and SVM used in lunar crater detection are investigated.The integrated craters detecting strategy combining Haar and PHOG features with AdaBoost and SVM classifiers is also studied.The method is proved to have high accuracy and recognition efficiency.Experimental rusults demonstrate that lunar crater recognition radio is proved by2%-5% via atlribute morphology and assemble classifier compared to traditional methods.
出处
《数据采集与处理》
CSCD
北大核心
2015年第6期1169-1176,共8页
Journal of Data Acquisition and Processing
基金
国家自然科学基金(61262031)资助项目
中国科学研究院地理研究院科研(YQZX-HT-KY-QT-20120119-1)资助项目
江西省高校科技落地计划(KJLD12067)资助项目
