电子学报2018，Vol.46Issue(4)：895-902,8.DOI:10.3969/j.issn.0372-2112.2018.04.018
光照不变量特征提取新方法及其在目标识别中的应用
A New Method for Extracting Illumination Invariant Features and Its Application in Target Recognition
摘要
Abstract
In order to solve the problem that LNSCT loses the target contour information due to discarding the low frequency components of image,a new illumination invariant extraction method,called MLNSCT,is proposed.Firstly,NSCT is used to decompose the input image in logarithm domain to realize the separation of the low-frequency and high-frequency components.Secondly,the BayesShrink threshold filter is applied to the high-frequency sub-band coefficients,and the inverse NSCT is performed for the low frequency components to obtain the feature image.Thirdly,a second NSCT decomposition on the feature image,the threshold filtering on high-frequency sub-band and inverse NSCT on low-frequency component are performed sequentially.After multiple NSCT decomposition,the illumination invariant features of the input image are extracted from the set of all high frequency sub-band coefficients.Through further study of the relationship between the illumination invariant features and the raw image,Dual Lenet,which is a parallel synchronous convolutional neural network,is designed to improve the accuracy of ground target recognition by fusing the high-level features of both.The experimental resuits show that MLNSCT has higher classification accuracy than that of LNSCT in Lenet model,and the classification accuracy is higher with the increase of decomposition number.Furthermore,it is proved that the fusion of illumination invariant features and raw image can effectively improve the classification accuracy of ground target recognition.关键词
光照不变量/非下采样轮廓波变换(NSCT)/多重对数域非下采样轮廓波变换(MLNSCT)/并行同步卷积神经网络/地面目标识别Key words
illumination invariant features/NSCT(Nonsubsampled Contourlet Transform)/MLNSCT(Multiple LNSCT)/dual Lenet/ground target recognition分类
信息技术与安全科学引用本文复制引用
李宝奇,贺昱曜,陈立柱..光照不变量特征提取新方法及其在目标识别中的应用[J].电子学报,2018,46(4):895-902,8.基金项目
国家自然科学基金(No.61271143) （No.61271143）
