HySARNet: a hybrid machine learning approach to synthetic aperture radar automatic target recognition

Soldin, Ryan J.; MacDonald, D.; Reisman, Matthew D.; Konz, Latisha; Rouse, R.; Overman, Timothy L.

doi:10.1117/12.2518155

Cited by 4 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [424], the authors involved a adversarial training technology to ensure the robustness of DL algorithm under the attacks of adversarial samples. HySARNet, as a hybrid ML model, was proposed in [429] to determine the robustness of model when faced variations in graze angle, resolution, and additive noise in SAR-ATR tasks. A wavelet kernel sparse deep coding network under unbalanced dataset was proposed in [430] for unbalanced PolSAR classification.…”

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

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

show abstract

“…CNNs have found application to synthetic aperture radar (SAR) in recent works using datasets such as the publicly available MSTAR dataset [16][17][18][19][20][21]. These studies typically used predefined network architectures that can contain many learnable parameters; other available architectures can contain upwards of a million parameters [22][23][24], which require significant amounts of memory.…”

Section: Introductionmentioning

confidence: 99%

Target Classification in Synthetic Aperture Radar Images Using Quantized Wavelet Scattering Networks

Raj

Fox

Narayanan

2021

Sensors

View full text Add to dashboard Cite

The need to classify targets and features in high-resolution imagery is of interest in applications such as detection of landmines in ground penetrating radar and tumors in medical ultrasound images. Convolutional neural networks (CNNs) trained using extensive datasets are being investigated recently. However, large CNNs and wavelet scattering networks (WSNs), which share similar properties, have extensive memory requirements and are not readily extendable to other datasets and architectures—and especially in the context of adaptive and online learning. In this paper, we quantitatively study several quantization schemes on WSNs designed for target classification using X-band synthetic aperture radar (SAR) data and investigate their robustness to low signal-to-noise ratio (SNR) levels. A detailed study was conducted on the tradeoffs involved between the various quantization schemes and the means of maximizing classification performance for each case. Thus, the WSN-based quantization studies performed in this investigation provide a good benchmark and important guidance for the design of quantized neural networks architectures for target classification.

show abstract