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SAR object classification using the DAE with a modified triplet restriction
A Multiple Radar Approach for Automatic Target Recognition of Aircraft using Inverse Synthetic Aperture Radar
Along with the improvement of radar technologies, Automatic Target
Recognition (ATR) using Synthetic Aperture Radar (SAR) and Inverse SAR (ISAR)
has come to be an active research area. SAR/ISAR are radar techniques to
generate a two-dimensional high-resolution image of a target. Unlike other
similar experiments using Convolutional Neural Networks (CNN) to solve this
problem, we utilize an unusual approach that leads to better performance and
faster training times. Our CNN uses complex values generated by a simulation to
train the network; additionally, we utilize a multi-radar approach to increase
the accuracy of the training and testing processes, thus resulting in higher
accuracies than the other papers working on SAR/ISAR ATR. We generated our
dataset with 7 different aircraft models with a radar simulator we developed
called RadarPixel; it is a Windows GUI program implemented using Matlab and
Java programming, the simulator is capable of accurately replicating a real
SAR/ISAR configurations. Our objective is to utilize our multi-radar technique
and determine the optimal number of radars needed to detect and classify
targets.Comment: 8 pages, 9 figures, International Conference for Data Intelligence
and Security (ICDIS
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
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 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
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
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
Hierarchical Disentanglement-Alignment Network for Robust SAR Vehicle Recognition
Vehicle recognition is a fundamental problem in SAR image interpretation.
However, robustly recognizing vehicle targets is a challenging task in SAR due
to the large intraclass variations and small interclass variations.
Additionally, the lack of large datasets further complicates the task. Inspired
by the analysis of target signature variations and deep learning
explainability, this paper proposes a novel domain alignment framework named
the Hierarchical Disentanglement-Alignment Network (HDANet) to achieve
robustness under various operating conditions. Concisely, HDANet integrates
feature disentanglement and alignment into a unified framework with three
modules: domain data generation, multitask-assisted mask disentanglement, and
domain alignment of target features. The first module generates diverse data
for alignment, and three simple but effective data augmentation methods are
designed to simulate target signature variations. The second module
disentangles the target features from background clutter using the
multitask-assisted mask to prevent clutter from interfering with subsequent
alignment. The third module employs a contrastive loss for domain alignment to
extract robust target features from generated diverse data and disentangled
features. Lastly, the proposed method demonstrates impressive robustness across
nine operating conditions in the MSTAR dataset, and extensive qualitative and
quantitative analyses validate the effectiveness of our framework
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