Complex Background SAR Target Recognition Based on Convolution Neural Network

Zhou, Yuanyuan; Chen, Tingjun; Tian, Jinchuan; Zhou, Zenan; Wang, Chen; Yang, Xiaqing; Shi, Jun

doi:10.1109/apsar46974.2019.9048279

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…A TL-based top-2 smooth loss function with cost-sensitive parameters was introduced to tackle the problems of label noise and imbalanced classes in [422]. A CNN-based recognition method of synthetic SAR dataset with complex background was proposed in [423]. As for noise and signal phase errors, the authors proposed a advanced DL based adversarial training method to mitigate these influence in [424].…”

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

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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.

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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

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“…Neural networks are slow to train [35][36][37][38] Even though the number of researches on shadow effect in SAR terrain classification is not so many, the shadow for SAR target classification still drew researchers' attention. For a single target in SAR image, shadows on the ground sometimes represent much more detailed information about the target's profile, which is a critical characteristic for target classification problems [41][42][43][44][45]. Besides, the disadvantages of SAR shadow in SAR terrain classification turns into the advantage for SAR target detection, which is used in application like ground moving target indication (GMTI) that researchers utilize the movement of target's shadow on the ground to detect the corresponding trace [46][47][48][49].…”

Section: Introductionmentioning

confidence: 99%

Dark Area Classification From Airborne SAR Images Based on Deep Margin Learning

Zhu¹

2021

IEEE Access

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The dark area in the SAR image corresponds to the zero-energy electromagnetic echo region because specular reflection occurs when the emitted electromagnetic wave (EMW) comes into contact with a smooth object on the ground. In addition, EWM sometimes cannot measure the entire portion of the ground object due to the incident angle of the actual aperture radar sensor and the height of the object on the ground, which creates a dark area in the SAR image called a SAR shadow. Therefore, it is challenging to distinguish the dark area from shadows or some ground objects with smooth surfaces. To address this problem, this paper proposes a novel deep marginal learning algorithm (DMLA) to implement the dark zone classification of airborne SAR images. Specifically, a novel Snake Search Algorithm (SSA) with a chain code is deployed to generate margin slices of dark areas in SAR images, which is used as the input to the following classification network. In addition, a deep convolutional neural network with simple architecture is constructed to train the margin slices of the SSA, which outputs the final classification results with labels. Finally, experiments on three public airborne SAR image datasets output the average classification accuracy of 98.775% for the dark margin slices and an accuracy of 100% to identify dark regions in the original SAR images. The proposed method not only contributes to a high classification accuracy but also improves time efficiency.

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“…As an advanced image processing technique, deep neural networks have been used in SAR image detection [26][27][28], classification [29][30][31][32], and filtering [33,34] in recent years. Since the shadows of different targets, especially with similar sizes, are similar and difficult to be tracked, deep networks provide a potential way for shadow tracking for video-SAR.…”

Section: Introductionmentioning

confidence: 99%

Ground Moving Target Tracking and Refocusing Using Shadow in Video-SAR

et al. 2020

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Stable and efficient ground moving target tracking and refocusing is a hard task in synthetic aperture radar (SAR) data processing. Since shadows in video-SAR indicate the actual positions of moving targets at different moments without any displacement, shadow-based methods provide a new approach for ground moving target processing. This paper constructs a novel framework to refocus ground moving targets by using shadows in video-SAR. To this end, an automatic-registered SAR video is first obtained using the video-SAR back-projection (v-BP) algorithm. The shadows of multiple moving targets are then tracked using a learning-based tracker, and the moving targets are ultimately refocused via a proposed moving target back-projection (m-BP) algorithm. With this framework, we can perform detecting, tracking, imaging for multiple moving targets integratedly, which significantly improves the ability of moving-target surveillance for SAR systems. Furthermore, a detailed explanation of the shadow of a moving target is presented herein. We find that the shadow of ground moving targets is affected by a target’s size, radar pitch angle, carrier frequency, synthetic aperture time, etc. With an elaborate system design, we can obtain a clear shadow of moving targets even in X or C band. By numerical experiments, we find that a deep network, such as SiamFc, can easily track shadows and precisely estimate the trajectories that meet the accuracy requirement of the trajectories for m-BP.

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Complex Background SAR Target Recognition Based on Convolution Neural Network

Cited by 3 publications

References 10 publications

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

Dark Area Classification From Airborne SAR Images Based on Deep Margin Learning

Ground Moving Target Tracking and Refocusing Using Shadow in Video-SAR

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