2,684 research outputs found
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
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SAR object classification using the DAE with a modified triplet restriction
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
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
A Hybrid Neural Network Framework and Application to Radar Automatic Target Recognition
Deep neural networks (DNNs) have found applications in diverse signal
processing (SP) problems. Most efforts either directly adopt the DNN as a
black-box approach to perform certain SP tasks without taking into account of
any known properties of the signal models, or insert a pre-defined SP operator
into a DNN as an add-on data processing stage. This paper presents a novel
hybrid-NN framework in which one or more SP layers are inserted into the DNN
architecture in a coherent manner to enhance the network capability and
efficiency in feature extraction. These SP layers are properly designed to make
good use of the available models and properties of the data. The network
training algorithm of hybrid-NN is designed to actively involve the SP layers
in the learning goal, by simultaneously optimizing both the weights of the DNN
and the unknown tuning parameters of the SP operators. The proposed hybrid-NN
is tested on a radar automatic target recognition (ATR) problem. It achieves
high validation accuracy of 96\% with 5,000 training images in radar ATR.
Compared with ordinary DNN, hybrid-NN can markedly reduce the required amount
of training data and improve the learning performance
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