Multi-view Deep CNN for Automated Target Recognition and Classification of Synthetic Aperture Radar Image

Chakraborty, Sudeshna; Amrita, Amrita; Choudhury, Tanupriya; Sille, Roohi; Dutta, Chiranjit; Dewangan, Bhupesh Kumar

doi:10.12720/jait.13.5.413-422

Cited by 19 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Overall, the radial sizes of ships can be efficiently estimated using the estimated ship HRRPs, which can then be utilized to classify ships. In addition to target HRRPs, synthetic aperture radar (SAR) images of targets can visually provide more intuitive target information, and target classification and recognition methods based on SAR images are also effective tools to classify and recognize targets 14 .…”

Section: Discussionmentioning

confidence: 99%

An efficient method for estimating complex high-resolution range profiles of ships

Su,

Shui,

2024

Fifteenth International Conference on Signal Processing Systems (ICSPS 2023)

View full text Add to dashboard Cite

High-resolution range profiles (HRRPs) of ships are significant for ship classification and recognition, sparse recovery methods are a major tool to attain ship HRRPs from high-resolution radar echoes. Aiming at the problem that complex HRRPs of ships are not finely modeled in the sparse recovery via iterative minimization (SRIM) method, this paper proposes a new SRIM method to estimate complex HRRPs of ships from radar echoes corrupted by non-Gaussian sea clutter. In the new SRIM method, ship HRRPs are modeled by a bi-parametric lognormal distribution (LND), and high-resolution sea clutter is characterized by the compound-Gaussian distribution with inverse Gaussian texture (CG-IG). The new SRIM method is compared with the classic sparse learning via iterative minimization (SLIM) method, the linear programming-based (LP-based) method, and the recent SRIM method by adopting simulated and measured radar data, the experimental results show that the new SRIM method effectively reduces the central processing unit (CPU) time of ship HRRP estimation while attaining better performance than the existing methods.

show abstract

Section: Discussionmentioning

confidence: 99%

An efficient method for estimating complex high-resolution range profiles of ships

Su,

Shui,

2024

Fifteenth International Conference on Signal Processing Systems (ICSPS 2023)

View full text Add to dashboard Cite

show abstract

“…In formula 3.11,F is the FP rate, and is the amount of correct target instances predicted from the wrong target [24]. The calculation way of AP is shown in formula 3.12.…”

Section: R = T P T P + F Nmentioning

confidence: 99%

Application of CNN based Regognition Technology in Public English Teaching in College Intelligent Classroom

2024

SCPE

View full text Add to dashboard Cite

With the advancement of intelligent technology, CNN-based recognition technology has been integrated into public English teaching in universities. This implementation contributes to enhancing teaching quality, nurturing students’ English proficiency, and holds significant educational and practical value. To address the issue of low traditional attendance efficiency in intelligent classrooms for public English teaching, a face recognition model based on CNN recognition technology has been developed. R-CNN is utilized for object detection, along with pyramid pooling and non-maximum suppression to acquire the optimal candidate region for face detection. Furthermore, K-Means clustering is combined to enhance Fast R-CNN, thereby improving detection accuracy. Experimental results demonstrated that among the three networks - Fast R-CNN, Faster R-CNN, and CNN -Faster R-CNN maintained a high recognition rate and exhibited faster convergence speed, showcasing superior overall performance. Specifically, at 500 iterations, the three networks require 23.7 seconds, 26.8 seconds, and 34.2 seconds, respectively. For facial expression recognition, Faster R-CNN achieved the highest recognition rate, indicating its exceptional detection efficiency and potential for aiding teaching management. This study offers novel technical support for public English teaching in intelligent university classrooms, effectively enhancing teaching efficacy and learning experiences. Its practical significance extends to promoting educational reform and improvement.

show abstract

“…The experimental data included images from the MSTAR dataset [35][36][37]. The MSTAR data were collected using the Sandia National Laboratories SAR sensor platform with X-band SAR sensors, having a resolution of 0.3 m in the spotlight mode [38]. The publicly available MSTAR dataset consisted of ten different categories of ground targets, including armored vehicles (BMP-2, BRDM-2, BTR-60, and BTR-70), tanks (T-62, T-72), rocket launchers (2S1), anti-aircraft units (ZSU-234), trucks (ZIL-131), and bulldozers (D7).…”

Section: Experimental Descriptionmentioning

confidence: 99%

A GAN-Based Augmentation Scheme for SAR Deceptive Jamming Templates with Shadows

Lang,

Li,

Liu

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

To realize fast and effective synthetic aperture radar (SAR) deception jamming, a high-quality SAR deception jamming template library can be generated by performing sample augmentation on SAR deception jamming templates. However, the current sample augmentation schemes of SAR deception jamming templates face certain problems. First, the authenticity of the templates is low due to the lack of speckle noise. Second, the generated templates have a low similarity to the target and shadow areas of the input templates. To solve these problems, this study proposed a sample augmentation scheme based on generative adversarial networks, which can generate a high-quality library of SAR deception jamming templates with shadows. The proposed scheme solved the two aforementioned problems from the following aspects. First, the influence of the speckle noise was considered in the network to avoid the problem of reduced authenticity in the generated images. Second, a channel attention mechanism module was used to improve the network’s learning ability of the shadow features, which improved the similarity between the generated template and the shadow area in the input template. Finally, the single generative adversarial network (SinGAN) scheme, which is a generative adversarial network capable of image sample augmentation for a single SAR image, and the proposed scheme were compared regarding the equivalent number of looks and the structural similarity between the target and shadow in the sample augmentation results. The comparison results demonstrated that, compared to the templates generated by the SinGAN scheme, those generated by the proposed scheme had targets and shadow features similar to those of the original image and could incorporate speckle noise characteristics, resulting in a higher authenticity, which helps to achieve fast and effective SAR deception jamming.

show abstract

Multi-view Deep CNN for Automated Target Recognition and Classification of Synthetic Aperture Radar Image

Cited by 19 publications

References 0 publications

An efficient method for estimating complex high-resolution range profiles of ships

An efficient method for estimating complex high-resolution range profiles of ships

Application of CNN based Regognition Technology in Public English Teaching in College Intelligent Classroom

A GAN-Based Augmentation Scheme for SAR Deceptive Jamming Templates with Shadows

Contact Info

Product

Resources

About