基于分类器决策融合的红外图像目标识别方法

提出基于分类器决策融合的红外图像目标识别问题。采用稀疏表示分类（Sparse representation-based classification，SRC）和卷积神经网络（Convolutional neural network，CNN）作为基础分类器。对于测试样本，首先基于SRC进行分类，并根据输出的决策变量判断决策可靠性。当判定识别结果可靠时，则识别过程结束，输出目标类别。反之，根据SRC的结果遴选部分置信度较高的候选类别，并在下一阶段针对这一步类别采用CNN进行确认分类。此外，将CNN的输出结果与SRC进行线性加权融合处理，根据融合结果做出最后的目标类别决策。提出方法通过综合SRC和CNN两者分类器的优点，综合提升红外目标识别性能。同时，这种层次化的决策融合方式避免了对所有样本的两次分类过程，整体上能够保证识别算法的效率。实验采用五类日常生活中常见的车辆目标红外图像进行，分别设置了原始样本条件、噪声样本条件以及遮挡样本条件。通过与部分现有方法进行对比，结果反映了提出方法的有效性和可靠性。

Infrared image target recognition method based on decision fusion of classifiers

The problem of infrared image target recognition based on classifier decision fusion was proposed. The sparse representation-based classification (SRC) and convolutional neural network (CNN) were used as the basic classifiers. For the test sample, it was first classified based on SRC, and the reliability of the decision was judged based on the output decision variables. When it was determined that the recognition result is reliable, the recognition process ended and the target category was output. On the contrary, some candidate categories with higher confidence were selected according to the results of SRC, and CNN was employed to confirm the classification result in the next stage. In addition, the CNN output result and SRC were subjected to linear weighted fusion processing, and the final target category decision was made according to the fusion result. The proposed method integrated the advantages of both SRC and CNN classifiers to comprehensively improve the performance of infrared target recognition. At the same time, this hierarchical decision fusion method avoided the two classification processes for all samples, and could ensure the overall efficiency of the recognition algorithm. The experiment was carried out using five types of infrared images of common vehicle targets in daily life, and the original sample conditions, noise sample conditions and occlusion sample conditions were set respectively. By comparing with some existing methods, the results reflect the effectiveness and reliability of the proposed method.