Optimizing the Reference Network by Minimum Spanning Tree Approach in SAR Tomography

Synthetic aperture radar tomography (TomoSAR) is widely used for 3-D imaging in urban environments. Reference network (RN) technology has emerged as an effective solution in TomoSAR, leveraging a spatially correlated network of persistent scatterers (PSs) to mitigate the atmospheric phase screen (APS), a significant source of error in multipass spaceborne SAR systems. An effective RN should connect sufficient PSs with reliable edges, thereby ensuring comprehensive tomographic imaging of PSs and other scatterers around PSs. However, RN construction faces challenge in simultaneously addressing both network connectivity and quality during RN construction. To resolve this issue, this article introduces an innovative approach for constructing an optimal RN based on the minimum spanning tree (MST-RN). In this approach, the paradigm for determining the optimal RN is transformed into the problem of finding the MST in which the relative elevation estimation quality of the edge in RN is regarded as their weight in MST. Furthermore, standardized metrics are proposed to comprehensively evaluate the effectiveness of RN. The proposed MST-RN method demonstrates superior effectiveness on the metrics including overall connectivity quality, PS candidate (PSC) coverage, and network redundancy, compared to existing methodologies, which underscores the significant progress made in the RN construction. Finally, the effectiveness of the proposed method is validated through experiments conducted on the TerraSAR-X dataset acquired in Shenzhen, China, demonstrating accurate height estimation of PSs in urban areas.