Persistent Scatterer Pixel Selection Method Based on Multi-Temporal Feature Extraction Network

Highlights: What are the main findings? The Multi-Temporal Feature Extraction Network (MFN) combining the 3D U-Net and the convolutional long short-term memory (CLSTM) is proposed for persistent scatterer (PS) pixel selection. The MFN can better balance the quality and quantity of PS pixel selection results compared to traditional ADI-based method, ultimately leading to significantly improved deformation measurement results. What is the implication of the main finding? The combination of the 3D U-Net and the CLSTM in MFN delivers superior spatiotemporal characteristics and extraction capability, ensuring the selection of a large number of high-quality PS pixels. This study provides a valuable reference for future research initiatives. As an end-to-end network, the MFN can directly utilize time-series SAR image raw data with automated characteristics extraction. This study provides an option requiring less human intervention for PS pixel selection. Persistent scatterer (PS) pixel selection is crucial in the PS-InSAR technique, ensuring the quality and quantity of PS pixels for accurate deformation measurements. However, traditional methods like the amplitude dispersion index (ADI)-based method struggle to balance the quality and quantity of PS pixels. To adequately select high-quality PS pixels, and thus improve the deformation measurement performance of PS-InSAR, the multi-temporal feature extraction network (MFN) is constructed in this paper. The MFN combines the 3D U-Net and the convolutional long short-term memory (CLSTM) to achieve time-series analysis. Compared with traditional methods, the proposed MFN can fully extract the spatiotemporal characteristics of complex SAR images to improve PS pixel selection performance. The MFN was trained with datasets constructed by reliable PS pixels estimated by the ADI-based method with a low threshold using ∼350 time-series Sentinel-1A SAR images, which contain man-made objects, farmland, parkland, wood, desert, and waterbody areas. To test the validity of the MFN, a deformation measurement experiment was designed for Tongzhou District, Beijing, China with 38 SAR images obtained by Sentinel-1A. Moreover, the similar time-series interferometric pixel (STIP) index was introduced to evaluate the phase stability of selected PS pixels. The experimental results indicate a significant improvement in both the quality and quantity of selected PS pixels, as well as a higher deformation measurement accuracy, compared to the traditional ADI-based method.