A Convolutional Neural Network Combined with Attributed Scattering Centers for SAR ATR

It is very common to apply convolutional neural networks (CNNs) to synthetic aperture radar (SAR) automatic target recognition (ATR). However, most of the SAR ATR methods using CNN mainly use the image features of SAR images and make little use of the unique electromagnetic scattering characteristics of SAR images. For SAR images, attributed scattering centers (ASCs) reflect the electromagnetic scattering characteristics and the local structures of the target, which are useful for SAR ATR. Therefore, we propose a network to comprehensively use the image features and the features related to ASCs for improving the performance of SAR ATR. There are two branches in the proposed network, one extracts the more discriminative image features from the input SAR image; the other extracts physically meaningful features from the ASC schematic map that reflects the local structure of the target corresponding to each ASC. Finally, the high-level features obtained by the two branches are fused to recognize the target. The experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset prove the capability of the SAR ATR method proposed in this letter.

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A Concurrent and Hierarchy Target Learning Architecture for Classification in SAR Application

Sensors ◽

10.3390/s18103218 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3218

Author(s):

Mohamed Touafria ◽

Qiang Yang

Keyword(s):

State Of The Art ◽

Automatic Target Recognition ◽

Image Features ◽

Training Data ◽

Synthetic Aperture ◽

Learning Networks ◽

Sar Images ◽

Stationary Target ◽

Fully Connected ◽

New Framework

This article discusses the issue of Automatic Target Recognition (ATR) on Synthetic Aperture Radar (SAR) images. Through learning the hierarchy of features automatically from a massive amount of training data, learning networks such as Convolutional Neural Networks (CNN) has recently achieved state-of-the-art results in many tasks. To extract better features about SAR targets, and to obtain better accuracies, a new framework is proposed: First, three CNN models based on different convolution and pooling kernel sizes are proposed. Second, they are applied simultaneously on the SAR images to generate image features via extracting CNN features from different layers in two scenarios. In the first scenario, the activation vectors obtained from fully connected layers are considered as the final image features; in the second scenario, dense features are extracted from the last convolutional layer and then encoded into global image features through one of the commonly used feature coding approaches, which is Fisher Vectors (FVs). Finally, different combination and fusion approaches between the two sets of experiments are considered to construct the final representation of the SAR images for final classification. Extensive experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset are conducted. Experimental results prove the capability of the proposed method, as compared to several state-of-the-art methods.

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Robust Automatic Target Recognition Algorithm for Large-Scene SAR Images and Its Adaptability Analysis on Speckle

Scientific Programming ◽

10.1155/2016/3801053 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Hongqiao Wang ◽

Yanning Cai ◽

Guangyuan Fu ◽

Shicheng Wang

Keyword(s):

Target Recognition ◽

Low Frequency ◽

Automatic Target Recognition ◽

Recognition Algorithm ◽

Scale Transformation ◽

Sar Image ◽

Sar Images ◽

Stationary Target ◽

Multilevel Sampling ◽

Process Method

Aiming at the multiple target recognition problems in large-scene SAR image with strong speckle, a robust full-process method from target detection, feature extraction to target recognition is studied in this paper. By introducing a simple 8-neighborhood orthogonal basis, a local multiscale decomposition method from the center of gravity of the target is presented. Using this method, an image can be processed with a multilevel sampling filter and the target’s multiscale features in eight directions and one low frequency filtering feature can be derived directly by the key pixels sampling. At the same time, a recognition algorithm organically integrating the local multiscale features and the multiscale wavelet kernel classifier is studied, which realizes the quick classification with robustness and high accuracy for multiclass image targets. The results of classification and adaptability analysis on speckle show that the robust algorithm is effective not only for the MSTAR (Moving and Stationary Target Automatic Recognition) target chips but also for the automatic target recognition of multiclass/multitarget in large-scene SAR image with strong speckle; meanwhile, the method has good robustness to target’s rotation and scale transformation.

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Product Dictionary Learning-Based SAR Target Configuration Recognition

International Journal of Antennas and Propagation ◽

10.1155/2020/9704829 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Ming Liu ◽

Shichao Chen ◽

Fugang Lu ◽

Junsheng Liu

Keyword(s):

Synthetic Aperture Radar ◽

Sparse Representation ◽

Dictionary Learning ◽

Synthetic Aperture ◽

Sar Image ◽

Product Model ◽

Sar Images ◽

Stationary Target ◽

Target Configuration ◽

Key Factor

Dictionary construction is a key factor for the sparse representation- (SR-) based algorithms. It has been verified that the learned dictionaries are more effective than the predefined ones. In this paper, we propose a product dictionary learning (PDL) algorithm to achieve synthetic aperture radar (SAR) target configuration recognition. The proposed algorithm obtains the dictionaries from a statistical standpoint to enhance the robustness of the proposed algorithm to noise. And, taking the inevitable multiplicative speckle in SAR images into account, the proposed algorithm employs the product model to describe SAR images. A more accurate description of the SAR image results in higher recognition rates. The accuracy and robustness of the proposed algorithm are validated by the moving and stationary target acquisition and recognition (MSTAR) database.

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SAR Target Configuration Recognition via Product Sparse Representation

Sensors ◽

10.3390/s18103535 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3535

Author(s):

Ming Liu ◽

Shichao Chen ◽

Fugang Lu ◽

Mengdao Xing

Keyword(s):

Sparse Representation ◽

State Of The Art ◽

Speckle Noise ◽

Synthetic Aperture ◽

Sar Image ◽

Product Model ◽

Sar Images ◽

Stationary Target ◽

Target Configuration ◽

Sparse Vector

Sparse representation (SR) has been verified to be an effective tool for pattern recognition. Considering the multiplicative speckle noise in synthetic aperture radar (SAR) images, a product sparse representation (PSR) algorithm is proposed to achieve SAR target configuration recognition. To extract the essential characteristics of SAR images, the product model is utilized to describe SAR images. The advantages of sparse representation and the product model are combined to realize a more accurate sparse representation of the SAR image. Moreover, in order to weaken the influences of the speckle noise on recognition, the speckle noise of SAR images is modeled by the Gamma distribution, and the sparse vector of the SAR image is obtained from q statistical standpoint. Experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) database. The experimental results validate the effectiveness and robustness of the proposed algorithm, which can achieve higher recognition rates than some of the state-of-the-art algorithms under different circumstances.

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SAR ATR of Ground Vehicles Based on ESENet

Remote Sensing ◽

10.3390/rs11111316 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1316 ◽

Cited By ~ 10

Author(s):

Li Wang ◽

Xueru Bai ◽

Feng Zhou

Keyword(s):

Target Recognition ◽

Recognition Rate ◽

Automatic Target Recognition ◽

Synthetic Aperture ◽

Ground Vehicles ◽

Feature Maps ◽

Sar Images ◽

Angle Variation ◽

Stationary Target ◽

Depression Angle

In recent studies, synthetic aperture radar (SAR) automatic target recognition (ATR) algorithms that are based on the convolutional neural network (CNN) have achieved high recognition rates in the moving and stationary target acquisition and recognition (MSTAR) dataset. However, in a SAR ATR task, the feature maps with little information automatically learned by CNN will disturb the classifier. We design a new enhanced squeeze and excitation (enhanced-SE) module to solve this problem, and then propose a new SAR ATR network, i.e., the enhanced squeeze and excitation network (ESENet). When compared to the available CNN structures that are designed for SAR ATR, the ESENet can extract more effective features from SAR images and obtain better generalization performance. In the MSTAR dataset containing pure targets, the proposed method achieves a recognition rate of 97.32% and it exceeds the available CNN-based SAR ATR algorithms. Additionally, it has shown robustness to large depression angle variation, configuration variants, and version variants.

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Realizing Target Detection in SAR Images Based on Multiscale Superpixel Fusion

Sensors ◽

10.3390/s21051643 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1643

Author(s):

Ming Liu ◽

Shichao Chen ◽

Fugang Lu ◽

Mengdao Xing ◽

Jingbiao Wei

Keyword(s):

Synthetic Aperture Radar ◽

False Alarm ◽

False Alarm Rate ◽

Target Detection ◽

Synthetic Aperture ◽

False Alarms ◽

Sar Image ◽

Constant False Alarm Rate ◽

Sar Images ◽

Complex Scenes

For target detection in complex scenes of synthetic aperture radar (SAR) images, the false alarms in the land areas are hard to eliminate, especially for the ones near the coastline. Focusing on the problem, an algorithm based on the fusion of multiscale superpixel segmentations is proposed in this paper. Firstly, the SAR images are partitioned by using different scales of superpixel segmentation. For the superpixels in each scale, the land-sea segmentation is achieved by judging their statistical properties. Then, the land-sea segmentation results obtained in each scale are combined with the result of the constant false alarm rate (CFAR) detector to eliminate the false alarms located on the land areas of the SAR image. In the end, to enhance the robustness of the proposed algorithm, the detection results obtained in different scales are fused together to realize the final target detection. Experimental results on real SAR images have verified the effectiveness of the proposed algorithm.

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Automatic Target Recognition for Low Resolution Foliage Penetrating SAR Images Using CNNs and GANs

Remote Sensing ◽

10.3390/rs13040596 ◽

2021 ◽

Vol 13 (4) ◽

pp. 596

Author(s):

David Vint ◽

Matthew Anderson ◽

Yuhao Yang ◽

Christos Ilioudis ◽

Gaetano Di Caterina ◽

...

Keyword(s):

Target Recognition ◽

Automatic Target Recognition ◽

Generative Adversarial Networks ◽

Low Resolution ◽

Sar Images ◽

Adversarial Networks ◽

Technological Advances ◽

Dataset Size ◽

Resolution Imaging ◽

High Level

In recent years, the technological advances leading to the production of high-resolution Synthetic Aperture Radar (SAR) images has enabled more and more effective target recognition capabilities. However, high spatial resolution is not always achievable, and, for some particular sensing modes, such as Foliage Penetrating Radars, low resolution imaging is often the only option. In this paper, the problem of automatic target recognition in Low Resolution Foliage Penetrating (FOPEN) SAR is addressed through the use of Convolutional Neural Networks (CNNs) able to extract both low and high level features of the imaged targets. Additionally, to address the issue of limited dataset size, Generative Adversarial Networks are used to enlarge the training set. Finally, a Receiver Operating Characteristic (ROC)-based post-classification decision approach is used to reduce classification errors and measure the capability of the classifier to provide a reliable output. The effectiveness of the proposed framework is demonstrated through the use of real SAR FOPEN data.

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Synthetic-Aperture Radar Image Despeckling based on Improved Non-Local Means and Non-Subsampled Shearlet Transform

Information Technology And Control ◽

10.5755/j01.itc.49.3.23998 ◽

2020 ◽

Vol 49 (3) ◽

pp. 299-307

Author(s):

Zengguo Sun ◽

Rui Shi ◽

Wei Wei

Keyword(s):

Synthetic Aperture Radar Image ◽

Synthetic Aperture ◽

Sar Image ◽

Sar Images ◽

Shearlet Transform ◽

Local Means ◽

New Type ◽

Non Local ◽

Homogeneous Regions ◽

Transform Domains

When Synthetic-Aperture (SAR) image is transformed into wavelet domain and other transform domains, most of the coefficients of the image are small or zero. This shows that SAR image is sparse. However, speckle can be seen in SAR images. The non-local means is a despeckling algorithm, but it cannot overcome the speckle in homogeneous regions and it blurs edge details of the image. In order to solve these problems, an improved non-local means is suggested. At the same time, in order to better suppress the speckle effectively in edge regions, the non-subsampled Shearlet transform (NSST) is applied. By combining NSST with the improved non-local means, a new type of despeckling algorithm is proposed. Results show that the proposed algorithm leads to a satisfying performance for SAR images.

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Speckle Noise Filtering Using Back-Propagation Multi-Layer Perceptron Network in Synthetic Aperture Radar Image

Deep Learning and Neural Networks ◽

10.4018/978-1-7998-0414-7.ch028 ◽

2020 ◽

pp. 489-510

Author(s):

Khwairakpam Amitab ◽

Debdatta Kandar ◽

Arnab K. Maji

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Synthetic Aperture Radar ◽

Back Propagation ◽

Speckle Noise ◽

Synthetic Aperture ◽

Sar Image ◽

Multi Layer Perceptron ◽

Sar Images ◽

Artificial Neural

Synthetic Aperture Radar (SAR) are imaging Radar, it uses electromagnetic radiation to illuminate the scanned surface and produce high resolution images in all-weather condition, day and night. Interference of signals causes noise and degrades the quality of the image, it causes serious difficulty in analyzing the images. Speckle is multiplicative noise that inherently exist in SAR images. Artificial Neural Network (ANN) have the capability of learning and is gaining popularity in SAR image processing. Multi-Layer Perceptron (MLP) is a feed forward artificial neural network model that consists of an input layer, several hidden layers, and an output layer. We have simulated MLP with two hidden layer in Matlab. Speckle noises were added to the target SAR image and applied MLP for speckle noise reduction. It is found that speckle noise in SAR images can be reduced by using MLP. We have considered Log-sigmoid, Tan-Sigmoid and Linear Transfer Function for the hidden layers. The MLP network are trained using Gradient descent with momentum back propagation, Resilient back propagation and Levenberg-Marquardt back propagation and comparatively evaluated the performance.

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Automatic Target Recognition Strategy for Synthetic Aperture Radar Images Based on Combined Discrimination Trees

Computational Intelligence and Neuroscience ◽

10.1155/2017/7186120 ◽

2017 ◽

Vol 2017 ◽

pp. 1-18 ◽

Cited By ~ 2

Author(s):

Xiaohui Zhao ◽

Yicheng Jiang ◽

Tania Stathaki

Keyword(s):

Synthetic Aperture Radar ◽

Target Recognition ◽

Automatic Target Recognition ◽

Operating Conditions ◽

Synthetic Aperture ◽

Stationary Target ◽

Feature Sets ◽

Radar Images ◽

Feature Processing ◽

Aperture Radar

A strategy is introduced for achieving high accuracy in synthetic aperture radar (SAR) automatic target recognition (ATR) tasks. Initially, a novel pose rectification process and an image normalization process are sequentially introduced to produce images with less variations prior to the feature processing stage. Then, feature sets that have a wealth of texture and edge information are extracted with the utilization of wavelet coefficients, where more effective and compact feature sets are acquired by reducing the redundancy and dimensionality of the extracted feature set. Finally, a group of discrimination trees are learned and combined into a final classifier in the framework of Real-AdaBoost. The proposed method is evaluated with the public release database for moving and stationary target acquisition and recognition (MSTAR). Several comparative studies are conducted to evaluate the effectiveness of the proposed algorithm. Experimental results show the distinctive superiority of the proposed method under both standard operating conditions (SOCs) and extended operating conditions (EOCs). Moreover, our additional tests suggest that good recognition accuracy can be achieved even with limited number of training images as long as these are captured with appropriately incremental sample step in target poses.

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