Full-Aperture Azimuth Spatial-Variant Autofocus Based on Contrast Maximization for Highly Squinted Synthetic Aperture Radar

Huang, Darong; Guo, Xinrong; Zhang, Zenghui; Wang, Yu; Truong, Trieu‐Kien

doi:10.1109/tgrs.2019.2936432

Cited by 12 publications

(2 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many methods are improved to mitigate this obstacle, such as those presented in [19]. Another type of autofocus is achieved by using metrics that measure the best image quality indicators, such as image entropy [20][21][22], contrast [23], and sharpness [24,25]. While conventional autofocus algorithms can enhance the clarity of interferometric images by determining the optimal focus position, they may introduce phase distortion during this process.…”

Section: Introductionmentioning

confidence: 99%

A Modified High-Precision Imaging Algorithm Based on Imaging Plane Optimization with Minimum Entropy Applied to UAV SAR

Du,

Xie,

Yang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

The miniaturized and lightweight unmanned aerial vehicle synthetic aperture radar (UAV SAR) is gradually becoming a research hotspot. The motion errors of UAVs lead to a deviation from a straight flight path couple with an unknown elevation of the target area, which leads to a degradation in SAR image quality. To achieve high-precision SAR imaging results, external terrain elevation information should be utilized. However, such data are challenging to obtain and limited in accuracy. In response to this problem, a modified high-precision imaging algorithm based on imaging plane optimization with minimum entropy is proposed. The proposed algorithm makes good use of the nonlinear trajectory of the UAV, which is unfriendly to imaging. Then, the image entropy is taken into account as the evaluation metric to acquire an approximated optimization imaging plane. Finally, the BP imaging is performed on the optimization imaging plane. The proposed method does not rely on external terrain information. Instead, it makes full use of the non-linear trajectory of the UAV and autonomously generates the optimal imaging plane for different terrain areas. By doing so, it achieves high-precision imaging results. Simulations and actual measurements have validated the effectiveness and enhancement of the proposed method.

show abstract

Section: Introductionmentioning

confidence: 99%

A Modified High-Precision Imaging Algorithm Based on Imaging Plane Optimization with Minimum Entropy Applied to UAV SAR

Du,

Xie,

Yang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…With the development of radio technology, radar has not only been used in military fields such as target detection [1,2] and reconnaissance [3,4] but also plays an important role in weather forecasting [5], environmental protection [6,7], etc. Synthetic aperture radar (SAR) [8,9] is an efficient type of radar system, which can generate high resolution images on the moving platform, such as airplanes, satellites, etc. In the process of radar movement, the ground target is scanned by transmitting electromagnetic waves and reflects the radar echo signal.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network with a Learnable Spatial Activation Function for SAR Image Despeckling and Forest Image Analysis

et al. 2021

View full text Add to dashboard Cite

Synthetic aperture radar (SAR) images are often disturbed by speckle noise, making SAR image interpretation tasks more difficult. Therefore, speckle suppression becomes a pre-processing step. In recent years, approaches based on convolutional neural network (CNN) achieved good results in synthetic aperture radar (SAR) images despeckling. However, these CNN-based SAR images despeckling approaches usually require large computational resources, especially in the case of huge training data. In this paper, we proposed a SAR image despeckling method using a CNN platform with a new learnable spatial activation function, which required significantly fewer network parameters without incurring any degradation in performance over the state-of-the-art despeckling methods. Specifically, we redefined the rectified linear units (ReLU) function by adding a convolutional kernel to obtain the weight map of each pixel, making the activation function learnable. Meanwhile, we designed several experiments to demonstrate the advantages of our method. In total, 400 images from Google Earth comprising various scenes were selected as a training set in addition to 10 Google Earth images including athletic field, buildings, beach, and bridges as a test set, which achieved good despeckling effects in both visual and index results (peak signal to noise ratio (PSNR): 26.37 ± 2.68 and structural similarity index (SSIM): 0.83 ± 0.07 for different speckle noise levels). Extensive experiments were performed on synthetic and real SAR images to demonstrate the effectiveness of the proposed method, which proved to have a superior despeckling effect and higher ENL magnitudes than the existing methods. Our method was applied to coniferous forest, broad-leaved forest, and conifer broad-leaved mixed forest and proved to have a good despeckling effect (PSNR: 23.84 ± 1.09 and SSIM: 0.79 ± 0.02). Our method presents a robust framework inspired by the deep learning technology that realizes the speckle noise suppression for various remote sensing images.

show abstract

Multi-Subaperture Interference for SAR Autofocusing

He,

Deng,

Sun

et al. 2023

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

View full text Add to dashboard Cite

Full-Aperture Azimuth Spatial-Variant Autofocus Based on Contrast Maximization for Highly Squinted Synthetic Aperture Radar

Cited by 12 publications

References 53 publications

A Modified High-Precision Imaging Algorithm Based on Imaging Plane Optimization with Minimum Entropy Applied to UAV SAR

A Modified High-Precision Imaging Algorithm Based on Imaging Plane Optimization with Minimum Entropy Applied to UAV SAR

Convolutional Neural Network with a Learnable Spatial Activation Function for SAR Image Despeckling and Forest Image Analysis

Multi-Subaperture Interference for SAR Autofocusing

Contact Info

Product

Resources

About