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

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）