.png){kind=icon}
返回
A Parameter-Free Enhanced SS&E Algorithm Based on Deep Learning for Suppressing Azimuth Ambiguities
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2022.3231269.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Aliasing artifacts introduced by azimuth ambiguity seriously impact the interpretation of synthetic aperture radar images. To achieve parameter-free and fast azimuth ambiguity suppression, a novel deep learning model is designed to estimate the ambiguous signal intensity to total signal intensity ratio in the range-Doppler domain. This model does not depend on processing parameters and can be applied in any acquisition mode. The mean shift algorithm is applied to select less ambiguous subspectra according to the estimation result. The selected subspectra are restored to a full spectrum with an energy concentrated extrapolation method to preserve the resolution. The enhanced spectral selection and extrapolation algorithm overcomes the dependence on processing parameters, and experiments based on TerraSAR-X and Radarsat-2 images indicate that the proposed algorithm suppresses the azimuth ambiguity significantly.
Keyword:
Azimuth
Doppler effect
Estimation
Signal processing algorithms
Deep learning
Extrapolation
Synthetic aperture radar
Azimuth ambiguity suppression
deep learning
signal processing
synthetic aperture radar (SAR)
期刊
IF:
8.6
论文数:
2.1W
被引数:
10.7W
机构
引用论文
Immunoproteomic Approach for Screening Vaccine Candidates from Bacterial Outer Membrane Proteins
PREAI
分享收藏分享收藏SAR Target Classification Using the Multikernel-Size Feature Fusion-Based Convolutional Neural Network基于多核大小特征融合的卷积神经网络SAR目标分类
PREAI
分享收藏Crystal structure of bacterial L-arabinose 1-dehydrogenase in complex with L-arabinose and NADP+
PREAI
分享收藏Structural and Functional Studies of WlbA: A Dehydrogenase Involved in the Biosynthesis of 2,3-Diacetamido-2,3-dideoxy-d-mannuronic Acid,.png)
Biochemistry
OAAI
BiochemistryIF0
OAAI分享收藏
查看更多内容