Fast InSAR Time-Series Analysis Method in a Full-Resolution SAR Coordinate System: A Case Study of the Yellow River Delta

Abstract

Ground deformation is a major determinant of delta sustainability. Sentinel-1 Terrain Observation by Progressive Scans (TOPS) data are widely used in interferometric synthetic aperture radar (InSAR) applications to monitor ground subsidence. Due to the unparalleled mapping coverage and considerable data volume requirements, high-performance computing resources including graphics processing units (GPUs) are employed in state-of-the-art methodologies. This paper presents a fast InSAR time-series processing approach targeting Sentinel-1 TOPS images to process massive data with higher efficiency and resolution. We employed a GPU-assisted InSAR processing method to accelerate data processing. Statistically homogeneous pixel selection (SHPS) filtering was used to reduce noise and detect features in scenes with minimal image resolution loss. Compared to the commonly used InSAR processing software, the proposed method significantly improved the Sentinel-1 TOPS data processing efficiency. The feasibility of the method was investigated by mapping the surface deformation over the Yellow River Delta using SAR datasets acquired between January 2021 and February 2022. The findings indicate that several events of significant subsidence have occurred in the study area. Combined with the geological environment, underground brine and hydrocarbon extraction as well as sediment consolidation and compaction contribute to land subsidence in the Yellow River Delta.

A Reliable Observation Point Selection Method for GB-SAR in Low-Coherence Areas

“…In deformation measurement applications, "Permanent Scatterers (PS)" with relatively stable reflection properties are commonly chosen for observation and analysis. This method was first proposed [20] by Ferretti et al to overcome the drawbacks brought about by time [21] and geometric decorrelation [22]. Despite its widespread adoption [23,24], the PS method, which relies on amplitude discretization to pinpoint permanent scatterers, falls short in addressing phase instability issues, prevalent in areas of low coherence.…”

Retrospective Analysis of Glacial Lake Outburst Flood (GLOF) Using AI Earth InSAR and Optical Images: A Case Study of South Lhonak Lake, Sikkim

“…Yang et al (2022) provides a systematic analysis of the contributing factors that lead to GLOF disasters through the utilization of meteorological data, SAR, and optical images [10]. Currently, the rapid advancement of SAR platforms has resulted in explosive data growth [13], and the field of InSAR image processing is shifting towards big data analysis. The influx of remote sensing big data presents both opportunities and challenges in data processing, management, and analysis [14].…”

“…The emergence and evolution of high-performance computing (HPC) presents a novel approach to tackling the challenge of large-scale InSAR processing. As a quintessential technology in HPC, graphics processing units (GPUs) have attracted increasing attention from researchers owing to their potent parallel processing capabilities, high memory bandwidth, low power consumption, and excellent performance in handling computationally intensive problems [13]. Cloud computing can more effectively tackle these challenges by offering on-demand access to a shared pool of computing resources, allowing users to leverage the capabilities of distributed computing and storage without requiring substantial upfront investments in hardware and software infrastructure [16][17][18].…”