基于卷积神经网络的军事图像分类

由于军事背景下战场上不同目标的相似度极高以及复杂情况下的分类识别率不高,传统视觉特征的分类精度已不能满足要求。针对含有特定军事目标的大规模图像分类问题构造了一种新的基于主成分分析(Principal Components Analysis, PCA)白化的卷积神经网络结构,有效地降低数据间的相关性,加强学习能力,提高目标分类的准确率。利用大规模的军事图像数据集对该模型进行了识别精度评估,实验表明,与基于视觉特征的词袋模型以及经典的卷积神经网络分类算法相比,该算法对于军事目标的分类精度有明显提高。

Military Image Classification based on Convolutional Neural Network

The classification accuracy of the traditional visual features can not meet the requirements in the application of modern military affairs due to the extremely high similarity of the different objects in the battlefield and low recognition rate in complex conditions. This paper presented a new architecture of the convolutional neural network (CNN) based on PCA whitening for solving the classification problem of large-scale images which contain some specific military objects. It efficiently eliminates the relativity of sample data, enhances the learning ability and improves the accuracy of the object recognition. The new CNN model was tested and evaluated with the large-scale data from military images and compared with the traditional methods. The experiment results show that the algorithm has higher recognition rate on military object recognition.