CAJ | 학술논문

在大量航空航天遥感图像中，快速发现和统计飞机目标并对其进行准确定位，在军事和民用方面均具有重要意义。结合遥感图像特点，针对飞机目标的特征，文章设计了一种基于层次化的分类器的遥感图像飞机目标检测方法。首先用基于哈尔（Haar）特征的底层AdaBoost分类器快速去除大部分非目标区域；然后用基于梯度方向直方图（Histogram of Oriented Gradient，HOG）特征的顶层支持向量机（Support Vector Machine，SVM）分类器进行精细检测。在分辨率为1m的遥感图像数据集上的实验结果表明，层次化分类器在保证较高检测率的前提下，大大降低了虚警率，可以有效解决遥感图像飞机检测问题。
재대량항공항천요감도상중，쾌속발현화통계비궤목표병대기진행준학정위，재군사화민용방면균구유중요의의。결합요감도상특점，침대비궤목표적특정，문장설계료일충기우층차화적분류기적요감도상비궤목표검측방법。수선용기우합이（Haar）특정적저층AdaBoost분류기쾌속거제대부분비목표구역；연후용기우제도방향직방도（Histogram of Oriented Gradient，HOG）특정적정층지지향량궤（Support Vector Machine，SVM）분류기진행정세검측。재분변솔위1m적요감도상수거집상적실험결과표명，층차화분류기재보증교고검측솔적전제하，대대강저료허경솔，가이유효해결요감도상비궤검측문제。
Quick finding and counting aircraft objects and acquiring their accurate positions in a number of space and aviation remote sensing images, are of great significance both in military and civilian applica-tions.According to the characteristics of remote sensing images and the features of aircraft objects, a new method is designed for aircraft detection in remote sensing images which makes use of the hierarchical classi-fiers. Firstly, bottom AdaBoost classifiers based on Haar features are used to quickly filter out most of the non-aircraft areas.Then top SVM (Support Vector Machine) classifiers based on HOG (Histogram of Oriented Gradient) features are applied for fine recognition.The experimental results in remote sensing image dataset with the resolution of 1m show that hierarchical classifiers can greatly reduce the false alarm rate with high detection rate, so this method can effectively solve the problem of aircraft detection in remote sensing image.