针对图像数据高度非线性可分情况下的目标分类问题,在稀疏表示分类框架的基础上,提出了一种基于监督字典学习的核稀疏表示的目标识别算法.采用融合了多特征信息的协方差描述子作为图像的描述符;通过引入核技巧,使非线性的图像数据在高维空间变得线性可分;并把分类误差与重构误差同时引入目标函数,在监督字典学习框架下,使学习得到的字典判别性更强.利用加州大学默塞德分校提供的UCMerced遥感数据集以及自测的红外车辆数据集做了实验验证,在这两个数据集上算法的平均识别率分别达到了89.46％和93.98％,实现了对非线性可分目标的高精度分类.%Aiming at the problem of target classification in the case of highly nonlinear image,a novel target recognition algorithm via kernel sparse representation based on supervised dictionary learning was proposed under the framework of sparse representation classification,in which the covariance descriptor with multifeatures information was used as image descriptors.By introducing kernel tricks,the nonlinear image data could be linearly separable in high-dimensional space.The classification errors and reconstruction errors were introduced into the objective function simultaneously.Based on these,the learned dictionary was more discriminative under the framework of supervised dictionary learning.Experimental validations using the UCMerced remote sensing dataset (provided by University of California,Merced) and self-captured infrared vehicle datasets demonstrate that the average recognition rate of this algorithm on these two data sets reaches 89.46％ and 93.98％,respectively,which verifies that the proposed algorithm achieves high-precision classification of nonlinear separable objects.
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