Search engine for discovering works of Art, research articles, and books related to Art and Culture
Javascript must be enabled to continue!
SAR Image Generation Method Using DH-GAN for Automatic Target Recognition
View through CrossRef
In recent years, target recognition technology for synthetic aperture radar (SAR) images has witnessed significant advancements, particularly with the development of convolutional neural networks (CNNs). However, acquiring SAR images requires significant resources, both in terms of time and cost. Moreover, due to the inherent properties of radar sensors, SAR images are often marred by speckle noise, a form of high-frequency noise. To address this issue, we introduce a Generative Adversarial Network (GAN) with a dual discriminator and high-frequency pass filter, named DH-GAN, specifically designed for generating simulated images. DH-GAN produces images that emulate the high-frequency characteristics of real SAR images. Through power spectral density (PSD) analysis and experiments, we demonstrate the validity of the DH-GAN approach. The experimental results show that not only do the SAR image generated using DH-GAN closely resemble the high-frequency component of real SAR images, but the proficiency of CNNs in target recognition, when trained with these simulated images, is also notably enhanced.
Title: SAR Image Generation Method Using DH-GAN for Automatic Target Recognition
Description:
In recent years, target recognition technology for synthetic aperture radar (SAR) images has witnessed significant advancements, particularly with the development of convolutional neural networks (CNNs).
However, acquiring SAR images requires significant resources, both in terms of time and cost.
Moreover, due to the inherent properties of radar sensors, SAR images are often marred by speckle noise, a form of high-frequency noise.
To address this issue, we introduce a Generative Adversarial Network (GAN) with a dual discriminator and high-frequency pass filter, named DH-GAN, specifically designed for generating simulated images.
DH-GAN produces images that emulate the high-frequency characteristics of real SAR images.
Through power spectral density (PSD) analysis and experiments, we demonstrate the validity of the DH-GAN approach.
The experimental results show that not only do the SAR image generated using DH-GAN closely resemble the high-frequency component of real SAR images, but the proficiency of CNNs in target recognition, when trained with these simulated images, is also notably enhanced.
Related Results
Highmobility AlGaN/GaN high electronic mobility transistors on GaN homo-substrates
Highmobility AlGaN/GaN high electronic mobility transistors on GaN homo-substrates
Gallium nitride (GaN) has great potential applications in high-power and high-frequency electrical devices due to its superior physical properties.High dislocation density of GaN g...
Studies on the Influences of i-GaN, n-GaN, p-GaN and InGaN Cap Layers in AlGaN/GaN High-Electron-Mobility Transistors
Studies on the Influences of i-GaN, n-GaN, p-GaN and InGaN Cap Layers in AlGaN/GaN High-Electron-Mobility Transistors
Systematic studies were performed on the influence of different cap layers of i-GaN, n-GaN, p-GaN and InGaN on AlGaN/GaN high-electron-mobility transistors (HEMTs) grown on sapphi...
Research on SAR Image Target Recognition Based on Convolutional Neural Network
Research on SAR Image Target Recognition Based on Convolutional Neural Network
Abstract
A synthetic aperture radar (SAR) automatic target recognition can effectively improve the utilization efficiency of SAR image data. In order to improve the ...
A Convolutional Neural Network Combined with Attributed Scattering Centers for SAR ATR
A Convolutional Neural Network Combined with Attributed Scattering Centers for SAR ATR
It is very common to apply convolutional neural networks (CNNs) to synthetic aperture radar (SAR) automatic target recognition (ATR). However, most of the SAR ATR methods using CNN...
DMSC-GAN: A c-GAN-Based Framework for Super-Resolution Reconstruction of SAR Images
DMSC-GAN: A c-GAN-Based Framework for Super-Resolution Reconstruction of SAR Images
Synthetic Aperture Radar (SAR) imagery is significant in remote sensing, but the limited spatial resolution results in restricted detail and clarity. Current super-resolution metho...
Depth-aware salient object segmentation
Depth-aware salient object segmentation
Object segmentation is an important task which is widely employed in many computer vision applications such as object detection, tracking, recognition, and ret...
A hydrogeological approach in urban underground infrastructures
A hydrogeological approach in urban underground infrastructures
The competition for space in urban areas due to an exponential growth of population makes derground engineering plays a crucial role in the development of cities. Urban underground...
MSG-Point-GAN: Multi-Scale Gradient Point GAN for Point Cloud Generation
MSG-Point-GAN: Multi-Scale Gradient Point GAN for Point Cloud Generation
The generative adversarial network (GAN) has recently emerged as a promising generative model. Its application in the image field has been extensive, but there has been little rese...