针对永久散射体PSInSAR（Persistent Scatterer SAR Interferometry）算法流程的局限性，本文立足差分雷达干涉预处理和时序微变反演性能提升，提出了一种基于优化解空间的PSInSAR序贯处理算法。该算法能够实现新SAR影像无缝嵌入，避免时序PSInSAR整体数据处理流程溯源，实现微变参数的准实时更新。研究以南京明城墙文化遗产及其200 m缓冲区赋存环境为示范，基于2015-01—2018-02的32景降轨Cosmo-SkyMed条带影像，开展传统PSInSAR方法与优化解空间PSInSAR序贯方法形变反演与性能对比研究。结果表明，基于解空间优化的PSInSAR序贯方法简化了差分干涉流程，通过解空间搜索机制和结构改进，降低了算法时空复杂度，实现了未知参数求解约10倍效率提升。通过形变场精度交叉验证，发现两种方法形变估计结果吻合（总体误差在0—1 mm），证实了PSInSAR序贯方法的有效性与可靠性，并揭示其在遥感大数据时代文化遗产高精度、准实时微变监测中的应用潜力。

Human society has entered the big data era given the exponential growth of remote sensing data due to the emergence of higher resolution, frequent revisits, and multi-platform image acquisitions. This phenomenon raised challenges for Interferometric SAR (InSAR) and Multitemporal InSAR (MTInSAR) data processing in near real time. For instance, the traditional PSInSAR algorithm can no longer satisfy a fast response monitoring due to delay in deformation time series updating.To address the aforementioned technical limitations, we proposed a PSInSAR sequential processing algorithm characterized by the optimized searching-space to achieve the performance improvement of Differential InSAR (DInSAR) data preprocessing and MTInSAR parameter estimation. In this approach, new SAR acquisitions were seamlessly integrated into the reconstructed spatiotemporal baselines of interferograms. Then, a triple-level Delaunay network was established using the temporal coherence value (high, low, and decorrelated) on network edges. On the basis of the value inheritance from previous PSInSAR estimations, the unknown parameter estimation on PS candidates for the sequential PSInSAR was accelerated owing to the proposed searching strategy adopted to the triple-level coherence network edges. That is, the solution space was first sampled using a large searching step (for example, 10 times the measurement accuracy of unknown parameters) to determine the potential interval of the optimal solution. Then, the choice of network edge was determined on the basis of the maximum value of the temporal coherence, followed by a dedicated fine searching (with the step equivalent to the predetermined accuracy of unknown parameters) concentrating on the potential interval for the optimal inversion. Owing to the applied global-local searching strategy, the optimization of calculation efficiency and estimation accuracy can be achieved.We conducted a comparative investigation for the deformation estimation and performance assessment between the current PSInSAR and the proposed sequential PSInSAR methods using 32 scenes Cosmo SkyMed Stripmap images (in descending orbits and acquired from January 2015 to February 2018) covering the Nanjing Ming Dynasty City wall. Results indicate that a high efficiency of unknown parameter estimation (height, deformation, and thermal dilation) was obtained using the sequential PSInSAR with the adopted optimized searching-space approach, with the computation acceleration with approximately an order of 10 times. The cross comparison of the deformation velocity rates from both approaches reveals a consistent estimation as presented by the overall dispersion values ranging from 0 to 1 mm/a, which validates the feasibility and reliability of sequential PSInSAR in the deformation estimation.The driving force of detected deformation anomalies along three sections of the city wall was further exploited, providing new insights for the sustainable conservation of the heritage properties. This study implies the potential of the sequential PSInSAR method in the accurate, near real-time deformation monitoring, and preventive conservation of large-scale cultural heritage sites (i.e., Nanjing Dynasty City Wall), particularly on the emergence of big data.