基于数据融合的SAR图像目标识别算法

目前卷积神经网络已经在SAR目标识别领域得到了广泛应用, 然而, 由于SAR图像的目标样本数量过少, 以及图像相干斑噪声的存在, 使得网络不能充分的学习样本深层特征, 对网络的识别性能会造成一定的影响. 针对上述问题, 提出一种基于数据融合的目标识别方法, 算法首先对原始图像分别进行噪声抑制和边缘信息提取处理, 然后将处理后的两类特征信息进行数据融合, 将单通道灰度图像融合扩充至双通道图像来作为训练样本, 同时构建了一个高低层特征融合的卷积神经网络模型, 使用注意力机制来加强了对有用特征的学习, 实验结果显示, 该方法在MSTAR数据集上, 表现了对不同目标型号的优秀识别效果.

SAR Image Target Recognition Algorithm Based on Data Fusion

Convolutional neural networks have been widely used in SAR target recognition. However, due to the small number of target samples in SAR images and coherent speckle noise in images, the networks cannot fully learn the deep features of samples, which exerts a certain impact on the recognition performance of the networks. To address the above problems, this study proposes a data fusion-based target recognition method. The algorithm firstly suppresses noise and extracts edge information of the original image and then fuses the processed two types of feature information. It expands the single-channel grey-scale image fusion to a two-channel image as the training sample and constructs a convolutional neural network model with high- and low-layer features fused, which uses the attention mechanism to enhance the learning of useful features. The experimental results reveal that the method demonstrates excellent performance in the recognition of different target models on the MSTAR dataset.