Abstract:
Objectives The Lutan-1 (LT-1) synthetic aperture radar (SAR) satellite, the first group of L-band fully polarimetric civilian SAR satellites in China with interferometry as its core mission, is suitable for monitoring and emergency response to disasters such as earthquakes and landslides. However, due to the inaccuracy of the satellite's real-time orbit data, the initial registration precision of the LT-1 SAR images is not high, which easily leads to registration failure and affects the efficiency of the image-automated registration process.
Methods In response to this issue, this study proposes a LT-1 image initial registration method and processing strategy based on a scale invariant feature transform like (SIFT-Like) algorithm to enhance the registration success rate. Taking the Ms 6.2 earthquake in Jishishan, Gansu, on December 18, 2023, as an example, the algorithm’s reliability was verified, with the registration success rate increased from 34.5% to 100%, successfully obtaining the coseismic deformation field of this earthquake. Furthermore, the coseismic deformation results of Sentinel-1A/B for this earthquake were also acquired for precision validation and analysis of the LT-1 results.
Results Integration of LT-1 and Sentinel-1A/B results indicate that the earthquake was primarily characterized by uplift, with a maximum uplift of 6.3 cm, classifying it as a thrust earthquake. Using pre-earthquake Sentinel-1 ascending and descending orbit interferometric results, 195 and 179 landslides were respectively identified, and it was observed that the liquefaction landslide-debris flow triggered by the earthquake in Caotan Village had exhibited significant deformation before the event, with a deformation rate exceeding 9 mm/a. And the impact of irregular segmentation of LT-1 images on algorithm effectiveness is discussed, and the potential applications of LT-1 satellites in earthquake and geological hazard monitoring are highlighted.
Conclusions The proposed algorithm effectively eliminates the registration issue of the LT-1 satellite. With the increasing archive data of LT-1, this algorithm can better highlight its advantages. Combining the LT-1 data and the algorithm can better serve the deformation monitoring and emergency response of earthquakes and geological disasters in the future.
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