Image Initial Registration Algorithm for Lutan-1 Satellite Based on Scale-Invariant Feature Transform-Like Algorithm— A Case Study of the Jishishan Earthquake
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摘要:
陆探一号(Lutan-1, LT-1)合成孔径雷达(synthetic aperture radar,SAR)卫星是中国首个以干涉为核心任务的L波段全极化民用SAR卫星星座,适用于地震、滑坡等灾害的监测和应急响应。然而,受该卫星实时轨道数据不精确的影响,LT-1 SAR影像的初始配准精度不高,容易导致配准失败,影响影像自动化处理效率。针对该问题,提出基于类尺度不变特征变换的LT-1影像初配准算法和处理策略,旨在提高其配准成功率。以2023⁃12⁃18甘肃临夏回族自治州积石山Ms 6.2地震为例验证算法的可靠性,将配准成功率由34.5%提高到100%,成功获取了该地震的同震形变场,同时也获取了Sentinel-1A/B升、降轨形变结果,用于对LT-1结果进行精度验证和分析。综合LT-1和Sentinel-1A/B的结果表明,该地震以抬升为主,最大抬升量达6.3 cm,属于逆冲型地震。通过震前Sentinel-1升、降轨时序形变结果分别识别出195和179个滑坡,并发现该地震触发的草滩村液化滑坡-泥流在震前已出现明显形变,形变速率超过9 mm/a。并讨论了LT-1影像分幅不规则对算法有效性的影响,展望了LT-1卫星在地震及同震地质灾害监测领域的应用潜力。
Abstract:ObjectivesThe 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.
MethodsIn 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.
ResultsIntegration 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.
ConclusionsThe 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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感谢青海省自然资源遥感中心提供的Lutan⁃1卫星数据、欧洲航天局提供的Sentinel‐1A/B数据,本文中图件采用GMT 6.4.0绘图[35]。http://ch.whu.edu.cn/cn/article/doi/10.13203/j.whugis20240087
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表 1 LT-1和Sentinel-1卫星数据参数
Table 1 Parameters of LT-1 and Sentinel-1 Satellite Data
卫星 轨道方向 主影像时间/影像起始时间 从影像时间/影像终止时间 时间基线/d 空间基线/m 数量/个 LT⁃1(同震) 升轨 2023⁃12⁃18 2023⁃12⁃22 4 743.13 4 LT⁃1(算法实验) 升轨 2023⁃07⁃12 2024⁃03⁃24 22 Sentinel⁃1(同震) 升轨(T128) 2023⁃10⁃27 2023⁃12⁃26 60 64.28 2 降轨(T135) 2023⁃12⁃14 2023⁃12⁃26 12 -116.22 2 Sentinel⁃1(震前) 升轨(T128) 2021⁃01⁃10 2023⁃06⁃05 67 降轨(T135) 2021⁃01⁃05 2023⁃12⁃14 51 表 2 LT-1卫星数据初始偏移量获取方法比较/像素
Table 2 Comparison of LT-1 Satellite Data Initial Offset Acquisition Methods/Pixel
类别 几何配准法获取初始偏移量 目视法确定特征点初始偏移量 SIFT-Like自动确定特征点初始偏移量 距离向 方位向 距离向 方位向 距离向 方位向 主、从SAR影像初配准 1 138 8 321 1 110 8 300 1 140 8 290 去除地形相位时初配准 0 0 -6 400 5 399 表 3 识别的震前滑坡信息表
Table 3 Identified Pre-Seismic Landslide Information
轨道方向 数量/个 总面积/km2 最小面积/km2 最大面积/km2 重叠个数 升轨 195 83.19 0.085 6.28 93 降轨 176 76.95 0.085 8.78 -
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