Abstract
Synthetic aperture radar automatic target recognition (SAR-ATR) has made great progress in recent years. Most of the established recognition methods are supervised, which have strong dependence on image labels. However, obtaining the labels of radar images is expensive and time-consuming. In this paper, we present a semi-supervised learning method that is based on the standard deep convolutional generative adversarial networks (DCGANs). We double the discriminator that is used in DCGANs and utilize the two discriminators for joint training. In this process, we introduce a noisy data learning theory to reduce the negative impact of the incorrectly labeled samples on the performance of the networks. We replace the last layer of the classic discriminators with the standard softmax function to output a vector of class probabilities so that we can recognize multiple objects. We subsequently modify the loss function in order to adapt to the revised network structure. In our model, the two discriminators share the same generator, and we take the average value of them when computing the loss function of the generator, which can improve the training stability of DCGANs to some extent. We also utilize images of higher quality from the generated images for training in order to improve the performance of the networks. Our method has achieved state-of-the-art results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, and we have proved that using the generated images to train the networks can improve the recognition accuracy with a small number of labeled samples.
Highlights
Synthetic Aperture Radar (SAR) can acquire the images of non-cooperative moving objects, such as aircrafts, ships, and celestial objects over a long distance under all weather and all day, which is widely used in civil and military fields [1]
There are mainly two reasons for this: one is that convolutional neural networks (CNNs) is used as the classifier in our model, which can extract more abundant features than the traditional machine learning methods, such as label propagation (LP) and PS3VM-D, other GANs that consist of no CNNs, such as Triple-GAN and improved-GAN; the other is that we have introduced the noisy data learning theory, and it has been proved that the negative effect of noisy data can be reduced with this theory, and bring better recognition results
We presented a deep convolutional generative adversarial networks (DCGANs)-based semi-supervised learning framework for SAR automatic target recognition
Summary
Synthetic Aperture Radar (SAR) can acquire the images of non-cooperative moving objects, such as aircrafts, ships, and celestial objects over a long distance under all weather and all day, which is widely used in civil and military fields [1]. Machine learning methods commonly used for image recognition include support vector machines (SVM), AdaBoost, and Bayesian neural network [16,17,18,19,20,21,22,23,24,25]. Huan et al [21] used a non-negative matrix factorization (NFM) algorithm to extract feature vectors of SAR images, and combined SVM and Bayesian neural networks to classify feature vectors. In these cases, how to select and combine features is a difficult problem, and the preprocessing scheme is rather complex. These methods are not practice-friendly, they are somehow effective
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