Synthetic aperture radar (SAR) tomography is a powerful multibaseline technique allowing to reconstruct volume structures by using datasets of the same area with slightly different viewing angles. Nowadays, the reference network technique (RNT) is widely applied in SAR tomography to detect single and double scatterers with no need for preliminary removal of atmospheric phase screen (APS). Therein, reference network (RN) construction as the basis of RNT is vitally important for effective APS calibration to successfully detect persistent scatterers (PSs) over the entire scene. Aiming at whole-scene PS detection, RN should be constructed with global distribution throughout the whole built environment. For the moment, standard RNT cannot achieve this goal because of the damage to network connectivity in the RN construction process. In this article, an ant colony search algorithm (ACSA) is designed for whole-scene RN construction via step-by-step exploring and retaining reliable single scatterers around each ant. The effectiveness of the proposed ACSA for RN construction is demonstrated through the three-dimensional SAR tomography experiments on high-resolution TerraSAR-X SAR data stack in Shenzhen, China.

This letter introduces a reliable three-dimensional reconstruction for buildings using a small data stack in synthetic aperture radar tomography (TomoSAR) based on a revised Reference Network (RN) method. The RN method can realize atmospheric phase error correction by persistent scatterer (PS) phase differencing. However, when dealing with a limited number of images in a small data stack, the inaccurate inversion of relative elevation for local PS arcs can result in error propagation during network integration, leading to compromised tomography results. To address this issue, we have refined the traditional RN method by introducing a new geometric constraint between PSs. In the proposed method, we utilize the relative elevation estimated from PS arc differential signal to accurately determine the spatial distance between PSs. PS arcs that exceed the geometric constraint are eliminated to assist in adjusting the reference network. The effectiveness of this optimization method is demonstrated through TomoSAR experiments conducted on a high-resolution TerraSAR-X SAR data stack in Shenzhen, China. Consequently, the method presented in this letter is highly suitable for processing small data stacks compared to the previous RN method.

Synthetic aperture radar tomography (TomoSAR) has gained significant attention for three-dimensional (3D) imaging in urban environments. A notable limitation of traditional TomoSAR approaches is their primary focus on persistent scatterers (PSs), disregarding targets with temporal decorrelated characteristics. Temporal variations in coherence, especially in urban areas due to the dense population of buildings and artificial structures, can lead to a reduction in detectable PSs and suboptimal 3D reconstruction performance. The concept of partially coherent scatterers (PCSs) has been proven effective by capturing the partial temporal coherence of targets across the entire time baseline. In this study, an novel approach based on an iterative sub-network generation method is introduced to leverage PCSs for enhanced 3D reconstruction in dynamic environments. We propose a coherence constraint iterative variance analysis approach to determine the optimal temporal baseline range that accurately reflects the interferometric coherence of PCSs. Utilizing the selected PCSs, a 3D imaging technique that incorporates the iterative generation of sub-networks into the SAR tomography process is developed. By employing the PS reference network as a foundation, we accurately invert PCSs through the iterative generation of local star-shaped networks, ensuring a comprehensive coverage of PCSs in study areas. The effectiveness of this method for the height estimation of PCSs is validated using the TerraSAR-X dataset. Compared with traditional PS-based TomoSAR, the proposed approach demonstrates that PCS-based elevation results complement those from PSs, significantly improving 3D reconstruction in evolving urban settings.

Synthetic aperture radar tomography (TomoSAR) is widely used for three-dimensional (3D) 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 multi-pass 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 paper 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 effectiveness of RN. The proposed MST-RN method demonstrates superior effectiveness on the metrics including overall connectivity quality, PS candidate 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.