26 research outputs found
A deep deformable residual learning network for SAR images segmentation
Reliable automatic target segmentation in Synthetic Aperture Radar (SAR)
imagery has played an important role in the SAR fields. Different from the
traditional methods, Spectral Residual (SR) and CFAR detector, with the recent
adavance in machine learning theory, there has emerged a novel method for SAR
target segmentation, based on the deep learning networks. In this paper, we
proposed a deep deformable residual learning network for target segmentation
that attempts to preserve the precise contour of the target. For this, the
deformable convolutional layers and residual learning block are applied, which
could extract and preserve the geometric information of the targets as much as
possible. Based on the Moving and Stationary Target Acquisition and Recognition
(MSTAR) data set, experimental results have shown the superiority of the
proposed network for the precise targets segmentation
SAR ATR Method with Limited Training Data via an Embedded Feature Augmenter and Dynamic Hierarchical-Feature Refiner
Without sufficient data, the quantity of information available for supervised
training is constrained, as obtaining sufficient synthetic aperture radar (SAR)
training data in practice is frequently challenging. Therefore, current SAR
automatic target recognition (ATR) algorithms perform poorly with limited
training data availability, resulting in a critical need to increase SAR ATR
performance. In this study, a new method to improve SAR ATR when training data
are limited is proposed. First, an embedded feature augmenter is designed to
enhance the extracted virtual features located far away from the class center.
Based on the relative distribution of the features, the algorithm pulls the
corresponding virtual features with different strengths toward the
corresponding class center. The designed augmenter increases the amount of
information available for supervised training and improves the separability of
the extracted features. Second, a dynamic hierarchical-feature refiner is
proposed to capture the discriminative local features of the samples. Through
dynamically generated kernels, the proposed refiner integrates the
discriminative local features of different dimensions into the global features,
further enhancing the inner-class compactness and inter-class separability of
the extracted features. The proposed method not only increases the amount of
information available for supervised training but also extracts the
discriminative features from the samples, resulting in superior ATR performance
in problems with limited SAR training data. Experimental results on the moving
and stationary target acquisition and recognition (MSTAR), OpenSARShip, and
FUSAR-Ship benchmark datasets demonstrate the robustness and outstanding ATR
performance of the proposed method in response to limited SAR training data
SAR Ship Target Recognition via Selective Feature Discrimination and Multifeature Center Classifier
Maritime surveillance is not only necessary for every country, such as in
maritime safeguarding and fishing controls, but also plays an essential role in
international fields, such as in rescue support and illegal immigration
control. Most of the existing automatic target recognition (ATR) methods
directly send the extracted whole features of SAR ships into one classifier.
The classifiers of most methods only assign one feature center to each class.
However, the characteristics of SAR ship images, large inner-class variance,
and small interclass difference lead to the whole features containing useless
partial features and a single feature center for each class in the classifier
failing with large inner-class variance. We proposes a SAR ship target
recognition method via selective feature discrimination and multifeature center
classifier. The selective feature discrimination automatically finds the
similar partial features from the most similar interclass image pairs and the
dissimilar partial features from the most dissimilar inner-class image pairs.
It then provides a loss to enhance these partial features with more interclass
separability. Motivated by divide and conquer, the multifeature center
classifier assigns multiple learnable feature centers for each ship class. In
this way, the multifeature centers divide the large inner-class variance into
several smaller variances and conquered by combining all feature centers of one
ship class. Finally, the probability distribution over all feature centers is
considered comprehensively to achieve an accurate recognition of SAR ship
images. The ablation experiments and experimental results on OpenSARShip and
FUSAR-Ship datasets show that our method has achieved superior recognition
performance under decreasing training SAR ship samples
Crucial Feature Capture and Discrimination for Limited Training Data SAR ATR
Although deep learning-based methods have achieved excellent performance on
SAR ATR, the fact that it is difficult to acquire and label a lot of SAR images
makes these methods, which originally performed well, perform weakly. This may
be because most of them consider the whole target images as input, but the
researches find that, under limited training data, the deep learning model
can't capture discriminative image regions in the whole images, rather focus on
more useless even harmful image regions for recognition. Therefore, the results
are not satisfactory. In this paper, we design a SAR ATR framework under
limited training samples, which mainly consists of two branches and two
modules, global assisted branch and local enhanced branch, feature capture
module and feature discrimination module. In every training process, the global
assisted branch first finishes the initial recognition based on the whole
image. Based on the initial recognition results, the feature capture module
automatically searches and locks the crucial image regions for correct
recognition, which we named as the golden key of image. Then the local extract
the local features from the captured crucial image regions. Finally, the
overall features and local features are input into the classifier and
dynamically weighted using the learnable voting parameters to collaboratively
complete the final recognition under limited training samples. The model
soundness experiments demonstrate the effectiveness of our method through the
improvement of feature distribution and recognition probability. The
experimental results and comparisons on MSTAR and OPENSAR show that our method
has achieved superior recognition performance
Semi-Supervised SAR ATR Framework with Transductive Auxiliary Segmentation
Convolutional neural networks (CNNs) have achieved high performance in
synthetic aperture radar (SAR) automatic target recognition (ATR). However, the
performance of CNNs depends heavily on a large amount of training data. The
insufficiency of labeled training SAR images limits the recognition performance
and even invalidates some ATR methods. Furthermore, under few labeled training
data, many existing CNNs are even ineffective. To address these challenges, we
propose a Semi-supervised SAR ATR Framework with transductive Auxiliary
Segmentation (SFAS). The proposed framework focuses on exploiting the
transductive generalization on available unlabeled samples with an auxiliary
loss serving as a regularizer. Through auxiliary segmentation of unlabeled SAR
samples and information residue loss (IRL) in training, the framework can
employ the proposed training loop process and gradually exploit the information
compilation of recognition and segmentation to construct a helpful inductive
bias and achieve high performance. Experiments conducted on the MSTAR dataset
have shown the effectiveness of our proposed SFAS for few-shot learning. The
recognition performance of 94.18\% can be achieved under 20 training samples in
each class with simultaneous accurate segmentation results. Facing variances of
EOCs, the recognition ratios are higher than 88.00\% when 10 training samples
each class
An Entropy-Awareness Meta-Learning Method for SAR Open-Set ATR
Existing synthetic aperture radar automatic target recognition (SAR ATR)
methods have been effective for the classification of seen target classes.
However, it is more meaningful and challenging to distinguish the unseen target
classes, i.e., open set recognition (OSR) problem, which is an urgent problem
for the practical SAR ATR. The key solution of OSR is to effectively establish
the exclusiveness of feature distribution of known classes. In this letter, we
propose an entropy-awareness meta-learning method that improves the
exclusiveness of feature distribution of known classes which means our method
is effective for not only classifying the seen classes but also encountering
the unseen other classes. Through meta-learning tasks, the proposed method
learns to construct a feature space of the dynamic-assigned known classes. This
feature space is required by the tasks to reject all other classes not
belonging to the known classes. At the same time, the proposed
entropy-awareness loss helps the model to enhance the feature space with
effective and robust discrimination between the known and unknown classes.
Therefore, our method can construct a dynamic feature space with discrimination
between the known and unknown classes to simultaneously classify the
dynamic-assigned known classes and reject the unknown classes. Experiments
conducted on the moving and stationary target acquisition and recognition
(MSTAR) dataset have shown the effectiveness of our method for SAR OSR
SAR Ship Target Recognition Via Multi-Scale Feature Attention and Adaptive-Weighed Classifier
Maritime surveillance is indispensable for civilian fields, including
national maritime safeguarding, channel monitoring, and so on, in which
synthetic aperture radar (SAR) ship target recognition is a crucial research
field. The core problem to realizing accurate SAR ship target recognition is
the large inner-class variance and inter-class overlap of SAR ship features,
which limits the recognition performance. Most existing methods plainly extract
multi-scale features of the network and utilize equally each feature scale in
the classification stage. However, the shallow multi-scale features are not
discriminative enough, and each scale feature is not equally effective for
recognition. These factors lead to the limitation of recognition performance.
Therefore, we proposed a SAR ship recognition method via multi-scale feature
attention and adaptive-weighted classifier to enhance features in each scale,
and adaptively choose the effective feature scale for accurate recognition. We
first construct an in-network feature pyramid to extract multi-scale features
from SAR ship images. Then, the multi-scale feature attention can extract and
enhance the principal components from the multi-scale features with more
inner-class compactness and inter-class separability. Finally, the adaptive
weighted classifier chooses the effective feature scales in the feature pyramid
to achieve the final precise recognition. Through experiments and comparisons
under OpenSARship data set, the proposed method is validated to achieve
state-of-the-art performance for SAR ship recognition
SAR Target Image Generation Method Using Azimuth-Controllable Generative Adversarial Network
Sufficient synthetic aperture radar (SAR) target images are very important
for the development of researches. However, available SAR target images are
often limited in practice, which hinders the progress of SAR application. In
this paper, we propose an azimuth-controllable generative adversarial network
to generate precise SAR target images with an intermediate azimuth between two
given SAR images' azimuths. This network mainly contains three parts:
generator, discriminator, and predictor. Through the proposed specific network
structure, the generator can extract and fuse the optimal target features from
two input SAR target images to generate SAR target image. Then a similarity
discriminator and an azimuth predictor are designed. The similarity
discriminator can differentiate the generated SAR target images from the real
SAR images to ensure the accuracy of the generated, while the azimuth predictor
measures the difference of azimuth between the generated and the desired to
ensure the azimuth controllability of the generated. Therefore, the proposed
network can generate precise SAR images, and their azimuths can be controlled
well by the inputs of the deep network, which can generate the target images in
different azimuths to solve the small sample problem to some degree and benefit
the researches of SAR images. Extensive experimental results show the
superiority of the proposed method in azimuth controllability and accuracy of
SAR target image generation
When Deep Learning Meets Multi-Task Learning in SAR ATR: Simultaneous Target Recognition and Segmentation
With the recent advances of deep learning, automatic target recognition (ATR)
of synthetic aperture radar (SAR) has achieved superior performance. By not
being limited to the target category, the SAR ATR system could benefit from the
simultaneous extraction of multifarious target attributes. In this paper, we
propose a new multi-task learning approach for SAR ATR, which could obtain the
accurate category and precise shape of the targets simultaneously. By
introducing deep learning theory into multi-task learning, we first propose a
novel multi-task deep learning framework with two main structures: encoder and
decoder. The encoder is constructed to extract sufficient image features in
different scales for the decoder, while the decoder is a tasks-specific
structure which employs these extracted features adaptively and optimally to
meet the different feature demands of the recognition and segmentation.
Therefore, the proposed framework has the ability to achieve superior
recognition and segmentation performance. Based on the Moving and Stationary
Target Acquisition and Recognition (MSTAR) dataset, experimental results show
the superiority of the proposed framework in terms of recognition and
segmentation
SAR ATR under Limited Training Data Via MobileNetV3
In recent years, deep learning has been widely used to solve the bottleneck
problem of synthetic aperture radar (SAR) automatic target recognition (ATR).
However, most current methods rely heavily on a large number of training
samples and have many parameters which lead to failure under limited training
samples. In practical applications, the SAR ATR method needs not only superior
performance under limited training data but also real-time performance.
Therefore, we try to use a lightweight network for SAR ATR under limited
training samples, which has fewer parameters, less computational effort, and
shorter inference time than normal networks. At the same time, the lightweight
network combines the advantages of existing lightweight networks and uses a
combination of MnasNet and NetAdapt algorithms to find the optimal neural
network architecture for a given problem. Through experiments and comparisons
under the moving and stationary target acquisition and recognition (MSTAR)
dataset, the lightweight network is validated to have excellent recognition
performance for SAR ATR on limited training samples and be very computationally
small, reflecting the great potential of this network structure for practical
applications.Comment: 6 pages, 3 figures, published in 2023 IEEE Radar Conference
(RadarConf23