东南大学学报（自然科学版）2011，Vol.41Issue(5)：1008-1012,5.DOI:10.3969/j.issn.1001-0505.2011.05.022
采用监督特征学习的红外小目标检测
Small infrared target detection via supervised feature learning
摘要
Abstract
A supervised feature learning method is proposed for improving the detection probability and detection speed of small infrared target detection. Through analyzing the traits of small targets' neighborhood image, a statistical feature based on gray intensity distribution is defined for describing the difference between the targets and nontargets' neighborhood. Intensity extrema on global images are considered as training samples, and then a feature with the highest discriminability is extracted by supervised learning. Subsequently, a multi-stage classifier is designed in the feature space, which adopts logistic regression and relevance vector machine algorithms to detect targets via "target-non-target" classification. Experimental results indicate that for large scale images and with the same false alarm rate, the proposed method is of higher probability of detection and much faster detection speed than local filtering methods.关键词
小目标检测/灰度分布特征/相关向量机Key words
small target detection/ feature of gray intensity distribution/ relevance vector machine分类
信息技术与安全科学引用本文复制引用
许庆晗,金立左,费树岷..采用监督特征学习的红外小目标检测[J].东南大学学报（自然科学版）,2011,41(5):1008-1012,5.基金项目
航空科学基金资助项目(20080169003). （20080169003）
