SAR target recognition based on deep learning

Chen, Sizhe; Wang, Haipeng

doi:10.1109/dsaa.2014.7058124

Cited by 185 publications

(110 citation statements)

References 9 publications

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“…Chen et al [30] firstly indicated that one single convolutional layer could effectively extract SAR targets feature representation with unsupervised learning using randomly sampled SAR targets patches and achieve the accuracy of 84.7% in 10-class classification tasks. Morgan et al [31] proposed an architecture of three convolutional layers, following a fully connected layer of Softmax as a classifier, increasing the accuracy to 92.3%.…”

Section: Sar Target Recognition With Cnnsmentioning

confidence: 99%

Transfer Learning with Deep Convolutional Neural Network for SAR Target Classification with Limited Labeled Data

2017

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Abstract:Tremendous progress has been made in object recognition with deep convolutional neural networks (CNNs), thanks to the availability of large-scale annotated dataset. With the ability of learning highly hierarchical image feature extractors, deep CNNs are also expected to solve the Synthetic Aperture Radar (SAR) target classification problems. However, the limited labeled SAR target data becomes a handicap to train a deep CNN. To solve this problem, we propose a transfer learning based method, making knowledge learned from sufficient unlabeled SAR scene images transferrable to labeled SAR target data. We design an assembled CNN architecture consisting of a classification pathway and a reconstruction pathway, together with a feedback bypass additionally. Instead of training a deep network with limited dataset from scratch, a large number of unlabeled SAR scene images are used to train the reconstruction pathway with stacked convolutional auto-encoders (SCAE) at first. Then, these pre-trained convolutional layers are reused to transfer knowledge to SAR target classification tasks, with feedback bypass introducing the reconstruction loss simultaneously. The experimental results demonstrate that transfer learning leads to a better performance in the case of scarce labeled training data and the additional feedback bypass with reconstruction loss helps to boost the capability of classification pathway.

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Section: Sar Target Recognition With Cnnsmentioning

confidence: 99%

Transfer Learning with Deep Convolutional Neural Network for SAR Target Classification with Limited Labeled Data

2017

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“…Hence, besides the convolution and activation operations (i.e., nonlinearity) that are the building blocks of an image classification CNN, we may want to use the transformations that take into account the correlation properties of the images given by (31)-(32). Reports about successful application of deep learning to the problems of automated target recognition in standard SAR images are encouraging [24,25], but at the same time the scarcity of the real data and the difficulties in augmenting it with modelled data are recognized as a major problem [25,26].…”

Section: Discussionmentioning

confidence: 99%

Statistical characterization of scattering delay in synthetic aperture radar imaging

Gilman¹,

Tsynkov²

2020

Inverse Problems &Amp; Imaging

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Distinguishing between the instantaneous and delayed scatterers in synthetic aperture radar (SAR) images is important for target identification and characterization. To perform this task, one can use the autocorrelation analysis of coordinate-delay images. However, due to the range-delay ambiguity the difference in the correlation properties between the instantaneous and delayed targets may be small. Moreover, the reliability of discrimination is affected by speckle, which is ubiquitous in SAR images, and requires statistical treatment.Previously, we have developed a maximum likelihood based approach for discriminating between the instantaneous and delayed targets in SAR images. To test it, we employed simple statistical models. They allowed us to simulate ensembles of images that depend on various parameters, including aperture width and target contrast.In the current paper, we enhance our previously developed methodology by establishing confidence levels for the discrimination between the instantaneous and delayed scatterers. Our procedure takes into account the difference in thresholds for different target contrasts without making any assumptions about the statistics of those contrasts.

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“…In this section, we compare the performance of our method with that of the CNN [13], label propagation (LP) [19], and progressive semisupervised SVM with diversity (PS3VM-D) [18]. The CNN is a fully supervised algorithm which only utilizes the labelled samples to train the classification model.…”

Section: Methodsmentioning

confidence: 99%

“…The experimental result showed that the CNN method outperforms the Gabor feature extraction-based SVM method, which demonstrated a great potential of the CNN for SAR image recognition. A convolutional network was designed in [13] to automatically extract the features for SAR target recognition. Using the learned convolutional features, the accuracy of 84.7% was achieved on the 10 types of targets in the MSTAR dataset.…”

Section: Introductionmentioning

confidence: 99%

A Novel Active Semisupervised Convolutional Neural Network Algorithm for SAR Image Recognition

Gao

Yue

Wang

et al. 2017

Computational Intelligence and Neuroscience

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Convolutional neural network (CNN) can be applied in synthetic aperture radar (SAR) object recognition for achieving good performance. However, it requires a large number of the labelled samples in its training phase, and therefore its performance could decrease dramatically when the labelled samples are insufficient. To solve this problem, in this paper, we present a novel active semisupervised CNN algorithm. First, the active learning is used to query the most informative and reliable samples in the unlabelled samples to extend the initial training dataset. Next, a semisupervised method is developed by adding a new regularization term into the loss function of CNN. As a result, the class probability information contained in the unlabelled samples can be maximally utilized. The experimental results on the MSTAR database demonstrate the effectiveness of the proposed algorithm despite the lack of the initial labelled samples.

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SAR target recognition based on deep learning

Cited by 185 publications

References 9 publications

Transfer Learning with Deep Convolutional Neural Network for SAR Target Classification with Limited Labeled Data

Transfer Learning with Deep Convolutional Neural Network for SAR Target Classification with Limited Labeled Data

Statistical characterization of scattering delay in synthetic aperture radar imaging

A Novel Active Semisupervised Convolutional Neural Network Algorithm for SAR Image Recognition

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