论文标题
二维基于稀疏限制的INS INAR成像，并带有反射嵌入
Two Dimensional Sparse-Regularization-Based InSAR Imaging with Back-Projection Embedding
论文作者
论文摘要
干涉合成孔径雷达（INSAR）成像方法通常基于匹配过滤类型的算法，而无需考虑场景的特征，这会导致有限的成像质量。此外，后处理步骤是不可避免的，例如图像登记，扁平阶段删除和相位噪声过滤。为了解决这些问题，我们提出了一种新的INS INAR成像方法。首先，为了提高成像质量，我们在2D稀疏正规化上提出了一个新的成像框架基础，其中场景的特征是嵌入的。其次，为避免后处理步骤，我们建立了一个新的前向观察过程，其中嵌入了反向预测成像方法。第三，基于近端梯度下降算法提出了向前和向后的迭代解决方案方法。对模拟和测量数据进行的实验揭示了该方法的有效性。与常规方法相比，可以直接从原始回波获得更高质量的干涉图而无需后处理。此外，在采样不足的情况下，它也适用。
Interferometric Synthetic Aperture Radar (InSAR) Imaging methods are usually based on algorithms of match-filtering type, without considering the scene's characteristic, which causes limited imaging quality. Besides, post-processing steps are inevitable, like image registration, flat-earth phase removing and phase noise filtering. To solve these problems, we propose a new InSAR imaging method. First, to enhance the imaging quality, we propose a new imaging framework base on 2D sparse regularization, where the characteristic of scene is embedded. Second, to avoid the post processing steps, we establish a new forward observation process, where the back-projection imaging method is embedded. Third, a forward and backward iterative solution method is proposed based on proximal gradient descent algorithm. Experiments on simulated and measured data reveal the effectiveness of the proposed method. Compared with the conventional method, higher quality interferogram can be obtained directly from raw echoes without post-processing. Besides, in the under-sampling situation, it's also applicable.
