光照不变量特征提取新方法及其在目标识别中的应用

针对LNSCT光照不变量提取方法因舍弃低频分量而丢失目标轮廓信息的问题，本文提出了一种新的光照不变量提取方法MLNCST.新方法首先用NSCT将对数域的输入图像进行第一重多尺度分解，实现低频分量和高频分量的分离；其次对高频子带系数进行BayesShrink阈值滤波，低频分量做逆NSCT得到其特征图像；然后对特征图像进行第二重NSCT分解，并对分解后的高频子带阈值滤波以及低频分量逆NSCT；经多重NSCT分解，最后由多次分解后的高频子带系数集提取光照不变量特征.经进一步研究光照不变量特征与原始图像之间的关系，设计了并行同步卷积神经网络-Dual Lenet，通过融合两者的高层特征来提高地面目标识别的准确率.实验结果显示，在Lenet模型下，MLNSCT比LNSCT具有更高的分类准确率，并且随着分解重数的增加分类准确率更高；同时融合了光照不变量特征的Dual lenet能进一步提高地面目标识别准确率.

A New Method for Extracting Illumination Invariant Features and Its Application in Target Recognition

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 results 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.