SAR ATR in the phase history domain using deep convolutional neural networks

Official URL: http://dx.doi.org/10.1117/12.2325365

Synthetic aperture radar (SAR) automatic target recognition (ATR) has been an interesting topic of research for decades. Existing methods perform the ATR task after image formation. However, in principle, image formation does not provide any new information regarding the classification task and it may even cause some information loss. Motivated by this, in this paper, we examine two SAR ATR frameworks that work in the phase history domain. In the first framework, we feed the complex-valued phase histories to a deep convolutional neural network (CNN) directly, and in the second one, we perform image formation, phase removal, and phase history generation before feeding the data to the CNN. CNNs are known for their superior performance on image classification tasks. The effectiveness of CNNs is based on dependency patterns in a given input. Thus, the input of CNNs is not limited to images but any input exhibiting such dependencies. Since complex-valued phase histories also have such a structure, they can be the input of a CNN. We perform ATR experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) database and compare the results of image-based and phase history-based classification.