深度学习在SAR目标识别与地物分类中的应用

Deep Learning as Applied in SAR Target Recognition and Terrain Classification

深度卷积网络等深度学习算法变革了计算机视觉领域,在多种应用上的效果都超过了以往传统图像处理算法。该文简要回顾了将深度学习应用在SAR图像目标识别与地物分类中的工作。利用深度卷积网络从SAR图像中自动学习多层的特征表征,再利用学习到的特征进行目标检测与目标分类。将深度卷积网络应用于SAR目标分类数据集MSTAR上,10类目标平均分类精度达到了99%。针对带相位的极化SAR图像,该文提出了复数深度卷积网络,将该算法应用于全极化SAR图像地物分类,Flevoland 15类地物平均分类精度达到了95%。

Deep learning such as deep neural networks has revolutionized the computer vision area.Deep learning-based algorithms have surpassed conventional algorithms in terms of performance by a significant margin.This paper reviews our works in the application of deep convolutional neural networks to target recognition and terrain classification using the SAR image.A convolutional neural network is employed to automatically extract a hierarchic feature representation from the data,based on which the target recognition and terrain classification can be conducted.Experimental results on the MSTAR benchmark dataset reveal that deep convolutional network could achieve a state-of-the-art classification accuracy of 99%for the 10-class task.For a polarimetric SAR image classification,we propose complex-valued convolutional neural networks for complex SAR images.This algorithm achieved a state-of-the-art accuracy of 95%for the 15-class task on the Flevoland benchmark dataset.