深度特征联合表征的红外图像目标识别方法

针对红外图像目标识别问题，提出了联合卷积神经网络和联合稀疏表示的方法。卷积神经网络学习红外目标图像的深度特征，描述目标的多层次特性。不同深度特征可实现对目标不同特性的描述，因此具有良好的互补性。综合运用多层次深度特征，可为目标识别提供更为充分的信息。分类过程中，采用联合稀疏表示对待识别样本的多层次深度特征矢量进行表征，通过不同特征矢量之间的相关性约束提升整体表示精度。因此，联合稀疏表示在利用各层次深度特征的同时，充分考察了它们之间的内在关联。根据联合稀疏表示的输出结果，按照误差最小的原则判定输入样本的目标类别。实验基于中波红外（ MWIR）目标图像数据集开展，分别在原始测试样本、噪声测试样本以及少量训练样本3类条件下对提出方法进行了测试，并与4类现有红外目标识别方法进行了对比分析。实验结果表明，提出方法在设置的3类测试条件下均可以取得优势性能，表明其对于红外图像目标识别问题具有应用潜力。

Target recognition method of infrared imagery via joint representation of deep features

For the target recognition of infrared imagery,a method was proposed via the combination of convolutional neural network(CNN)and joint sparse representation(JSR).CNN learned the deep features of the infrared target imagery,which described the multi-layer properties of the target.Different layers of deep features described the target charateristics from differnt aspects,so they can well complement each other.The joint use of multi-layer deep features could provide more valid information for target recognition.During the classification,JSR was employed to represent the multi-level deep feature vectors and the inner correlations among different features was used to improve the overall representation precision.Therefore,JSR not only made use of individual deep features but also considered their inner correlations.According to the outs from JSR,the target label of the input sample was determined based on the minimum error.The experiments were conducted based on mid-wave infrared(MWIR)dataset under the conditions of original test samples,noise test samples,and small training set.Simultaneously,the proposed method was compared with four previous methods.According to the experimental results,the proposed method achieves better performance under the three conditions,validating its potential in infrared imagery target recognition.