A Deep Convolutional Generative Adversarial Networks (DCGANs)-Based Semi-Supervised Method for Object Recognition in Synthetic Aperture Radar (SAR) Images

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.

A Multi-Scale U-Shaped Convolution Auto-Encoder Based on Pyramid Pooling Module for Object Recognition in Synthetic Aperture Radar Images

“…The labelled benchmarks are too small to train a supervised deep network effectively, and overfitting caused by limited labelled samples is often one of the main causes of performance degradation of the supervised model. To handle this problem, various unsupervised DL models are employed and developed, including the autoencoder (AE) [9,10], the generative adversarial network (GAN) [11,12], and the restricted Boltzmann machine (RBM) [13]. Due to the fact of its simple implementation and attractive computational cost, the AE has widely been used in SAR ATR which minimizes the distortion between the inputs and the reconstructions to guarantee that the mapping process preserves the information of the inputs.…”

A SAR Target Recognition Based on Guided Reconstruction and Weighted Norm-Constrained Deep Belief Network

“…Based on transfer learning algorithm, it can transfer the learning knowledge of tagged SAR image to a new SAR target recognition task [9]. By the deep convolutional generative adversarial networks (DCGAN) which adopted a semi-supervised learning method can reduce the number of free parameters and the negative impact of mislabeled samples on network performance [10]. In addition, in the case of limited samples, the convolutional neural network (CNN) and the multi-scale feature extraction module are used for SAR image target recognition [11].…”

GPU-Based Soil Parameter Parallel Inversion for PolSAR Data

“…With the increase in the number of global satellites, the application of ground exploration has become increasingly common [6][7][8]. But low resolution is also a major problem for exploration [9,10]. The development of science and technology has promoted the rapid development of synthetic aperture radar (SAR) techniques, through which the quality and resolution of radar imaging has been significantly improved.…”