A Lightweight Fully Convolutional Neural Network for SAR Automatic Target Recognition

Yu, Jun; Zhou, Guangyu; Zhou, Shangbo; Yin, Jiajun

doi:10.3390/rs13153029

Cited by 18 publications

(5 citation statements)

References 37 publications

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“…Furthermore, the optical images of 10-class targets are shown in Figure 7 ; they are ground vehicles, carriers, and trucks (you can see more targets in SAR images and types of targets in Refs. [ 46 , 47 , 48 , 49 ]). The data had a spatial resolution of one foot.…”

Section: Methodsmentioning

confidence: 99%

Recognition of Targets in SAR Images Based on a WVV Feature Using a Subset of Scattering Centers

Lee

Kim

2022

Sensors

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This paper proposes a robust method for feature-based matching with potential for application to synthetic aperture radar (SAR) automatic target recognition (ATR). The scarcity of measured SAR data available for training classification algorithms leads to the replacement of such data with synthetic data. As attributed scattering centers (ASCs) extracted from the SAR image reflect the electromagnetic phenomenon of the SAR target, this is effective for classifying targets when purely synthetic SAR images are used as the template. In the classification stage, following preparation of the extracted template ASC dataset, some of the template ASCs were subsampled by the amplitude and the neighbor matching algorithm to focus on the related points of the test ASCs. Then, the subset of ASCs were reconstructed to the world view vector feature set, considering the point similarity and structure similarity simultaneously. Finally, the matching scores between the two sets were calculated using weighted bipartite graph matching and then combined with several weights for overall similarity. Experiments on synthetic and measured paired labeled experiment datasets, which are publicly available, were conducted to verify the effectiveness and robustness of the proposed method. The proposed method can be used in practical SAR ATR systems trained using simulated images.

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Section: Methodsmentioning

confidence: 99%

Recognition of Targets in SAR Images Based on a WVV Feature Using a Subset of Scattering Centers

Lee

Kim

2022

Sensors

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“…The weights were intuitive in their settings, with a maximum total score of 50. The assessment indicators result in Table 1 can be represented as (5).…”

Section: Methods 31 Proposed Approach In Deep Action Learningmentioning

confidence: 99%

“…A recent study introduced deep learning using camouflaged object detection with cascade and feedback fusion (CODCEF) [4] which can detect within 37 ms using an NVIDIA Jetson Nano device. Another [5] used data augmentation to perform camouflage detection. This method is considered adequate with 99% accuracy, superior in a lightweight but does not specify the computer specifications used.…”

Section: Introductionmentioning

confidence: 99%

Troop camouflage detection based on deep action learning

Muslikhin

Nasuha

Arifin

et al. 2022

IJ-AI

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<p><span lang="EN-US">Detecting troop camouflage on the battlefield is crucial to beat or decide in critical situations to survive. This paper proposed a hybrid model based on deep action learning for camouflage recognition and detection. To involve deep action learning in this proposed system, deep learning based on you only look once (YOLOv3) with SquezeeNet and the fourth steps on action learning were engaged. Following the successful formulation of the learning cycle, an instrument examines the environment and performance in action learning with qualitative weightings; specific target detection experiments with view angle, target localization, and the firing point procedure were performed. For each deep action learning cycle, the complete process is divided into planning, acting, observing, and reflecting. If the results do not meet the minimal passing grade after the first cycle, the cycle will be repeated until the system succeeds in the firing point. Furthermore, this study found that deep action learning could enhance intelligence over earlier camouflage detection methods, while maintaining acceptable error rates. As a result, deep action learning could be used in armament systems if the environment is properly identified.</span></p>

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“…Since Eq. W are represented by a 1×1 convolution kernel for actual operation, and their channel number is set to half the channel number [41]. This parameter setup reduces the computational cost by 50%.…”

Section: Attentional Mechanismsmentioning

confidence: 99%

Detection of SAR Image Multiscale Ship Targets in Complex Inshore Scenes Based on Improved YOLOv5

Wang,

Hou,

Xin

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Synthetic aperture radar (SAR) can operate around the clock and in all weather, and therefore high-resolution SAR images have been frequently applied for ship inspection. However, current ship target detection and identification methods have limited detection accuracy and lead to missing detection of small targets due to speckle noise caused by the imaging principle of SAR imagery and complex nearshore interference. Therefore, this paper proposes an improved YOLOv5 method to address the problem of low accuracy in multi-ship target detection tasks in complex scenes. The developed scheme enhances the ship target detection performance while reducing the number of parameters. Specifically, first, we improve the size of the input SAR images and optimize the anchor frames of ship targets in the training dataset to locate small target ships more accurately. Then, symmetric pyramidal non-local block(APNB) and Sim attention mechanism (SimAM) are introduced to reduce near-shore background interference. Additionally, to make the C3 module output richer and with more feature information, channel shuffling is performed after the C3 output to enhance the information exchange between channels. Finally, to reduce the number of parameters and computational cost during model training, the normal convolution in the NECK part is replaced with Ghost convolution. The index F1 of the proposed method on the High-Resolution SAR Image Dataset (HRSID) and SAR Ship Detection Dataset (SSDD) reaches the highest of 91.3% and 95.8%, respectively. MAPs (0.5:0.95) for the two datasets are both the highest, which are at least 2% higher than the suboptimal method. In selected specific inshore scenes, the ship detection performance of the proposed method outperforms current advanced methods for multiscale ships. It is shown that the proposed method can extract ship features effectively in complex scenes and its effectiveness is further validated on the large scene AIR-SARShip-1 dataset.

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A Lightweight Fully Convolutional Neural Network for SAR Automatic Target Recognition

Cited by 18 publications

References 37 publications

Recognition of Targets in SAR Images Based on a WVV Feature Using a Subset of Scattering Centers

Recognition of Targets in SAR Images Based on a WVV Feature Using a Subset of Scattering Centers

Troop camouflage detection based on deep action learning

Detection of SAR Image Multiscale Ship Targets in Complex Inshore Scenes Based on Improved YOLOv5

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