Deep Learning for Direct Automatic Target Recognition from SAR Data

Kazemi, Samia; Yonel, Bariscan; Yazıcı, Birsen

doi:10.1109/radar.2019.8835492

Cited by 14 publications

(9 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…network that simultaneously learned to detect, classify, and estimate the length of ships. Mullissa et al [116] showed that CNNs can be trained directly on Complex-Valued SAR data; Kazemi et al [117] performed object classification using an RNN based architecture directly on received SAR signal instead of processed SAR images; and Rostami et al [118] and Huang et al [119] explored knowledge transfer or transfer learning from other domains to the SAR domain for SAR object detection.…”

Section: B Object Detectionmentioning

confidence: 99%

Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives

Zhu

Montazeri

Ali

et al. 2021

IEEE Geosci. Remote Sens. Mag.

193

View full text Add to dashboard Cite

This is the pre-acceptance version, to read the final version please go to IEEE Geoscience and Remote Sensing Magazine on IEEE XPlore.Deep learning in remote sensing has become an international hype, but it is mostly limited to the evaluation of optical data. Although deep learning has been introduced in Synthetic Aperture Radar (SAR) data processing, despite successful first attempts, its huge potential remains locked. In this paper, we provide an introduction to the most relevant deep learning models and concepts, point out possible pitfalls by analyzing special characteristics of SAR data, review the state-of-the-art of deep learning applied to SAR in depth, summarize available benchmarks, and recommend some important future research directions. With this effort, we hope to stimulate more research in this interesting yet under-exploited research field and to pave the way for use of deep learning in big SAR data processing workflows.

show abstract

Section: B Object Detectionmentioning

confidence: 99%

Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives

Zhu

Montazeri

Ali

et al. 2021

IEEE Geosci. Remote Sens. Mag.

193

View full text Add to dashboard Cite

show abstract

“…Instead of continuing to update the encoded image estimation until convergence, we consider a fixed number of iteration steps within which the algorithm yields sufficiently accurate solution. L number of subsequent update steps in (10) are mapped into the stages of an L-layer RNN similarly to [27]- [29], [57], and the DL networks Z Θ (l) (.) here acts as the nonlinear activation functions of the RNN stages.…”

Section: Rnn Structurementioning

confidence: 99%

Unrolled Wirtinger Flow with Deep Decoding Priors for Phaseless Imaging

Kazemi¹,

Yonel²,

Yazıcı³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

We introduce a deep learning (DL) based network for imaging from measurement intensities. The network architecture uses a recurrent structure that unrolls the Wirtinger Flow (WF) algorithm with a deep prior which enables performing the algorithm updates in a lower dimensional encoded image space. We use a separate deep network (DN), referred to as the encoding network, for transforming the spectral initialization used in the WF algorithm to an appropriate initial value for the encoded domain. The unrolling scheme that models a fixed number of iterations of the underlying algorithm into a recurrent neural network (RNN) enable us to simultaneously learn the parameters of the prior network, the encoding network and the RNN during training. We establish sufficient conditions on the network to guarantee exact recovery under deterministic forward models and demonstrate the relation between the Lipschitz constants of the trained prior and encoding networks to the convergence rate. We show the practical applicability of our method on synthetic aperture imaging using high fidelity simulation data from the PCSWAT software. Our numerical study shows that the deep prior facilitates improvements in sample complexity.

show abstract

“…Then, the class of test sample was identified by the minimum distance between the center of class and learned features space of test sample. Similarly, a DNN model was employed in [450] to directly classify targets with slow-time and fast-time sampled signals.…”

Section: A Sar Images Processingmentioning

confidence: 99%

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

Lang,

Fu,

Martorella

et al. 2020

Preprint

View full text Add to dashboard Cite

Modern radar systems have high requirements in terms of accuracy, robustness and real-time capability when operating on increasingly complex electromagnetic environments. Traditional radar signal processing (RSP) methods have shown some limitations when meeting such requirements, particularly in matters of target classification. With the rapid development of machine learning (ML), especially deep learning, radar researchers have started integrating these new methods when solving RSP-related problems. This paper aims at helping researchers and practitioners to better understand the application of ML techniques to RSP-related problems by providing a comprehensive, structured and reasoned literature overview of ML-based RSP techniques. This work is amply introduced by providing general elements of ML-based RSP and by stating the motivations behind them. The main applications of ML-based RSP are then analysed and structured based on the application field. This paper then concludes with a series of open questions and proposed research directions, in order to indicate current gaps and potential future solutions and trends.

show abstract

Deep Learning for Direct Automatic Target Recognition from SAR Data

Cited by 14 publications

References 24 publications

Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives

Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives

Unrolled Wirtinger Flow with Deep Decoding Priors for Phaseless Imaging

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

Contact Info

Product

Resources

About