Automatic Target Recognition in Synthetic Aperture Radar Imagery: A State-of-the-Art Review

El-Darymli, Khalid; Gill, Eric W.; McGuire, Peter; Power, Des; Moloney, Cecilia

doi:10.1109/access.2016.2611492

Cited by 322 publications

(159 citation statements)

References 111 publications

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“…Generally, the SAR-ATR system can be split into three stages: detection, low-level classification (LLC) and high-level classification (HLC). The first two stages that are also known as prescreening and discrimination together generate the focus-of-attention (FOA) module [1]. It interfaces with the input SAR images and outputs a list of potential SAR targets as the input of the HLC stage.…”

Section: Introductionmentioning

confidence: 99%

“…Various methods have been proposed to implement the HLC stage, which can be concluded as three taxonomies: feature-based, model-based and semi-model-based according to [1]. Feature-based approaches, extracting and preprocessing features from SAR target chips and training a classifier with them, are extensively used in the literature for HLC stage.…”

Section: Introductionmentioning

confidence: 99%

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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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2017

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“…1 and . 2 are respectively the l 1 -norm and the l 2 -norm, denotes the error tolerance. The Equation (11) represents a multitask problem since X and Y have multiple atoms (columns).…”

Section: Recognition Via Multitask Sparse Frameworkmentioning

confidence: 99%

“…Both types are reconstructed according to the reflected electromagnetic waves of the target. Recently, the automatic target recognition (ATR) from these radar images has become an active research topic and it is of paramount importance in several military and civilian applications [1][2][3]. Therefore, it is crucial to develop a new robust generic algorithm that recognizes the aerial (aircraft) targets in ISAR images and the ground battlefield targets in SAR images.…”

Section: Introductionmentioning

confidence: 99%

Radar Target Recognition Using Salient Keypoint Descriptors and Multitask Sparse Representation

et al. 2018

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Abstract:In this paper, we propose a novel approach to recognize radar targets on inverse synthetic aperture radar (ISAR) and synthetic aperture radar (SAR) images. This approach is based on the multiple salient keypoint descriptors (MSKD) and multitask sparse representation based classification (MSRC). Thus, to characterize the targets in the radar images, we combine the scale-invariant feature transform (SIFT) and the saliency map. The purpose of this combination is to reduce the number of SIFT keypoints by keeping only those located in the target area (salient region); this speeds up the recognition process. After that, we compute the feature vectors of the resulting salient SIFT keypoints (MSKD). This methodology is applied for both training and test images. The MSKD of the training images leads to constructing the dictionary of a sparse convex optimization problem. To achieve the recognition, we adopt the MSRC taking into consideration each vector in the MSKD as a task. This classifier solves the sparse representation problem for each task over the dictionary and determines the class of the radar image according to all sparse reconstruction errors (residuals). The effectiveness of the proposed approach method has been demonstrated by a set of extensive empirical results on ISAR and SAR images databases. The results show the ability of the proposed method to predict adequately the aircraft and the ground targets.

show abstract

“…Both types are reconstructed according to the reflected electromagnetic waves of the target. Recently, the automatic target recognition (ATR) from these radar images has become an active research topic and it is of paramount importance in several military and civilian applications [1,2]. Therefore, it is crucial to develop a new robust generic algorithm that recognize the aerial (aircraft) targets in ISAR images and the ground battlefield targets in SAR images.…”

Section: Introductionmentioning

confidence: 99%

Radar Target Recognition using Salient Keypoint Descriptors and Multitask Sparse Representation

Karine¹,

Toumi²,

Khenchaf³

2018

Preprint

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Abstract:In this paper, we propose a novel approach to recognize radar targets on inverse synthetic aperture radar (ISAR) and synthetic aperture radar (SAR) images. This approach is based on the multiple salient keypoint descriptors (MSKD) and multitask sparse representation based classification (MSRC). Thus, to characterize the targets in the radar images, we combine the scale-invariant feature transform (SIFT) and the saliency map. The goal of this combination is to reduce the SIFT keypoints and their time computing time by maintaining only those located in the target area (salient region). Then, we compute the feature vectors of the resulting salient SIFT keypoints (MSKD). This methodology is applied for both training and test images. The MSKD of the training images leads to construct the dictionary of a sparse convex optimization problem. To achieve the recognition, we adopt the MSRC taking into consideration each vector in the MSKD as a task. This classifier solves the sparse representation problem for each task over the dictionary and determines the class of the radar image according to all sparse reconstruction errors (residuals). The effectiveness of the proposed approach method has been demonstrated by a set of extensive empirical results on ISAR and SAR images databases. The results show the ability of our method to predict adequately the aircraft and the ground targets.

show abstract

Automatic Target Recognition in Synthetic Aperture Radar Imagery: A State-of-the-Art Review

Cited by 322 publications

References 111 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

Radar Target Recognition Using Salient Keypoint Descriptors and Multitask Sparse Representation

Radar Target Recognition using Salient Keypoint Descriptors and Multitask Sparse Representation

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