Abstract: Conventional aircraft identification methods reduce the recognition rate when disturbed by many factors such as blurring, occlusion, noise and illumination, and the convolution neural network relies mainly on the local features while the important global structural features such as contour features are lost, which leads to poor performance of the algorithm in identifying disturbed aircraft images. Therefore, a joint surveillance and recognition method that combines local and global features based on dense convolutional neural network is proposed, and image features are obtained based on dense convolution neural networks and classified by combining local features (convolution neural network features) and global features (direction gradient histogram features). The classifier objective function uses softmax loss and central loss joint monitoring method. The experimental results show that the combination of local features and global features makes the algorithm more intelligent, and the loss function joint supervision method can achieve intra-class aggregation and inter-class dispersion of images deep features. This algorithm can effectively solve the problem of low recognition rate of convolution neural networks for remote sensing images that suffer from multiple disturbances.

Key words: densely connected CNN, target recognition, center loss, joint supervision；Histogram of Oriented Gradient（HOG）

摘要： 传统的飞机识别方法受模糊、遮挡、噪声以及光照等多种因素的干扰时会降低识别率，且卷积神经网络主要依赖局部特征，却丢失了轮廓特征等重要的全局结构化特征，从而导致算法对于受干扰飞机图像识别效果不佳。因此，基于密集卷积神经网络提出一种结合局部与全局特征的联合监督识别方法，以密集卷积神经网络为基础得到图像特征，通过结合局部特征（卷积神经网络特征）与全局特征（方向梯度直方图特征）进行分类，分类器目标函数使用softmax损失和中心损失联合监督方法。实验结果表明，局部特征与全局特征的结合使算法更加智能化，且损失函数联合监督方法能够实现图像深层特征的类内聚合、类间分散，该算法能有效解决卷积神经网络对受到多种干扰的遥感图像识别率低的问题。

关键词: 密集卷积神经网络, 目标识别, 中心损失, 联合监督, 方向梯度直方图