质量改进 合成孔径雷达 (SAR) 图像使用压缩传感 (CS) 与摩尔-罗斯反向 (MPI) 和先前从空间变异化 (SVA) 进行质量改进
IEEE transactions on pattern analysis and machine intelligence
|August 16, 2024
概括
这项研究引入了一种新的侧叶缩小方法,用于合成光圈雷达 (SAR) 和极度SAR (POLSAR),使用压缩传感 (CS) 和摩尔-罗斯反向 (MPI). 该方法有效地恢复主叶数据,并抑制侧叶,而不会影响极化签名.
科学领域:
- 遥感 遥感 遥感 遥感
- 信号处理 信号处理
- 图像分析 图像分析
背景情况:
- 压缩传感 (CS) 允许在已知非零样本位置时恢复数据.
- 摩尔-罗斯反向 (MPI) 是一个数学工具,用于在CS中恢复数据.
- 侧叶减小对于提高合成孔径雷达 (SAR) 和极度 SAR (POLSAR) 的图像质量至关重要.
研究的目的:
- 为SAR和POLSAR图像开发一种新的侧叶片缩小方法.
- 为了利用压力传感 (CS) 和摩尔-罗斯逆 (MPI) 来改进数据恢复和侧叶抑制.
- 通过大规模获得的太空和空载雷达数据来验证该方法的有效性.
主要方法:
- 利用对非零样本位置的先前知识来缩小CS测量矩阵.
- 应用摩尔-罗斯反向 (MPI) 与数据恢复的缩小矩阵.
- 采用空间变异apodization (SVA) 先验来识别主叶和侧叶区域,然后进行背景融合以恢复侧叶区域.
主要成果:
- 在标准的CS恢复和基于MPI的拟议方法之间证明了数学等价性.
- 在大规模SAR和POLSAR数据集中成功恢复主环数据和有效抑制侧环数据.
- 保持偏振特征,表明对数据特征没有不利影响.
结论:
- 拟议的基于MPI的CS方法为SAR和POLSAR图像的侧叶减少提供了一种有效的方法.
- 该技术适用于处理大量的雷达数据.
- 该方法实现了令人满意的侧叶抑制,同时保持了数据完整性.
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