准确识别卫星设备等拍摄到的待发射(或飞行途中)导弹类型, 实现及时有效防御, 是国内外**领域研究的热点之一。由于战争状态中导弹具有掩饰色, 且因外形差别不显著, 现有基于底层特征进行导弹分类识别难度较大甚至无法识别。针对这一问题, 提出一种基于稀疏自动编码器(Sparse Auto-Encoder, SAE)高层视觉特征融合底层特征提取的新算法, 为了提高分类精度, 引入迁移学习, 借助STL-10样本库局部特征, 并将导弹图像局部特征向量一并送入池化层卷积神经网络(Convolution Neural Network, CNN)提取导弹目标对象图像全局特征, 通过Softmax回归模型实现导弹分类识别。实验表明, 文中提出SAE融合底层特征的导弹分类识别算法较传统基于底层特征及SAE高层特征分类算法具有更高的准确性及鲁棒性。另外, 为了避免因新型导弹目标对象缺乏训练而导致分类性能下降甚至失效, 算法引入迁移学习实现局部特征提取, 实验验证了算法的可行性和准确性。

Accurate classification of missile by the missile image (or in flight state) taken through the satellite equipment, which achieve the timely and effective defense, is one of the hot spot in the military field at home and abroad. Because the missile in the war state has masked color, and the missile shape differences are not significant, it is difficult to classify the missile type based on the low level features. Aiming at these problems, a new algorithm was presented based on Sparse Auto-Encoder (SAE) combining the high level visual feature and low level feature extraction. In order to improve classification accuracy, transfer learning was introduced, with the help of the STL-10 sample database local features, the global features of small sample missile target image can be extracted through the local features by the convolution neural network (CNN) of pooling layer, and then transmitted into the Softmax regression model to realize classification of missiles. Experiments show that compared with the traditional low level vision features and SAE high level vision feature classification algorithm, the SAE fusion feature classification algorithm has higher accuracy and robustness. In addition, in order to avoid classification performance reduce even failure under the lack of training for new type missile target object, the new algorithm induces transfer learning to extract local feature, experimental result proves the feasibility and accuracy of the algorithm.