Interferogram filtering is an essential step in processing data from interferometric synthetic aperture radar (InSAR), which greatly improves the accuracy of terrain reconstruction and deformation monitoring. Most traditional interferogram filtering methods achieve noise suppression and detail preservation through morphological estimation based on the statistical properties of the interferogram in the spatial or frequency domain. However, as the interferogram’s spatial distribution is diverse and complex, traditional filtering methods struggle to adapt to different distribution and noise conditions and cannot handle detail preservation and noise suppression simultaneously. The study proposes a convolutional neural network (CNN)-based multi-level feature fusion model for interferogram filtering that differs from the traditional feedforward neural network (FNN). Adopting a multi-depth multi-path convolution strategy, the method preserves phase details and suppresses noise during interferogram filtering. In filtering experiments based on simulated data, qualitative and quantitative evaluations were used to validate the performance and generalization capabilities of the proposed method. The method’s applicability was evaluated by visual observation during filtering and unwrapping experiments on real data, and the time-series deformation acquired by time series (TS)-InSAR technique is used to evaluate the effect of interferogram filters on deformation monitoring accuracy. Compared to commonly used interferogram filtering methods, the proposed method has significant advantages in terms of performance and efficiency. The study findings suggest new directions for research on high-precision InSAR data processing and provide technical support for practical applications of InSAR.

中文翻译：

---

基于多级特征融合CNN的InSAR干涉图滤波方法

干涉图滤波是干涉合成孔径雷达（InSAR）数据处理中必不可少的步骤，极大地提高了地形重建和变形监测的准确性。传统的干涉图滤波方法大多基于干涉图在空间或频域的统计特性，通过形态估计来实现噪声抑制和细节保留。然而，由于干涉图的空间分布多样且复杂，传统的滤波方法难以适应不同的分布和噪声条件，不能同时处理细节保留和噪声抑制。该研究提出了一种不同于传统前馈神经网络（FNN）的基于卷积神经网络（CNN）的多级特征融合模型，用于干涉图滤波。该方法采用多深度多径卷积策略，在干涉图滤波过程中保留了相位细节并抑制了噪声。在基于模拟数据的过滤实验中，使用定性和定量评估来验证所提出方法的性能和泛化能力。在对真实数据进行滤波和展开实验时，通过目视观察评价该方法的适用性，并利用时间序列（TS）-InSAR技术获得的时间序列变形来评价干涉图滤波器对变形监测精度的影响。与常用的干涉图滤波方法相比，该方法在性能和效率方面具有显着优势。研究结果为高精度InSAR数据处理研究指明了新方向，为InSAR的实际应用提供了技术支持。