Landslide detection using polarimetric ALOS-2/PALSAR-2 data: a case study of 2016 Kumamoto earthquake in Japan
Landslide detection using polarimetric ALOS-2/PALSAR-2 data: a case study of 2016 Kumamoto earthquake in Japan
复制标题
DOI：
10.1117/12.2324030
复制
发表时间：
2018-10
期刊：
影响因子：
--
通讯作者：
T. Konishi;Y. Suga
中科院分区：
文献类型：
--
作者：
T. Konishi;Y. Suga
世界上每年都会发生由暴雨或地震引发的滑坡事件。遥感技术是滑坡测绘和监测的有效手段。合成孔径雷达(SAR)具有全天候、全天候的成像能力，具有很大的发展潜力。因此，利用合成孔径雷达数据进行快速损伤评估是可望的。在以前的研究中，我们使用COSMO-SkyMed HH单极化数据证明了滑坡检测是由事件前后的相关系数得出的。另一方面，与单极化合成孔径雷达数据相比，全极化合成孔径雷达数据包含了多种信息。在这项研究中，我们论证了从SAR图像中进行偏振分析在滑坡区域检测中的适用性。2016年日本熊本大地震造成熊本县山体滑坡破坏。自然灾害发生后的快速损失评估是快速应对危机的关键。使用了2015年12月3日、2015年4月21日、2016年4月21日和2016年5月5日获得的三个ALOS-2/PALSAR-2偏振数据。根据每个PolSAR数据计算了熵/α角/各向异性。还进行了PolSAR的山口四组分分解分析。极化相干性(γHH-V)由极化前后极化数据的HH和VV之间的相关性计算得到。在这项研究中，我们处理了利用PolSAR数据的事前和事后极化参数来检测滑坡。由于地震引发的滑坡使地表的森林植被消失，南麻生村最大的滑坡区域明显表现为地表散布。使用随机森林(RF)分类器，利用事前和事后的PolSAR数据检测滑坡的程度。结果表明，利用RF分类器从PolSAR数据中提取事前和事后的α角、熵和γHH-V是有效的滑坡检测方法。
Landslide events occur annually induced by heavy rain or an earthquake in the world. Remote sensing technique is an effective for landslide mapping and monitoring. Synthetic aperture radar (SAR) has a great potential due to its all-weather day and night imaging capabilities. Therefore, the utilization of SAR data for rapid damage assessment is expected. In a previous study, we demonstrated that landslide detection derived from correlation coefficient using pre- and post-event COSMO-SkyMed HH single polarization data. On the other hand, fully-polarimetric SAR (PolSAR) data contain various information compared to single polarization SAR data. In this study, we demonstrated the applicability of polarimetric analysis from SAR images for detection of the landslide area. The 2016 Kumamoto earthquake in Japan caused landslide damage in Kumamoto prefecture, Japan. Rapid damage assessment after natural disasters is crucial to fast crisis response. Three ALOS-2/PALSAR-2 polarimetric data acquired on 3 December, 2015, 21 April, 2016 and 5 May, 2016 were used. Entropy/α angle/anisotropy were calculated from each PolSAR data. Yamaguchi four-component decomposition analysis of PolSAR was also conducted. The polarimetric coherence (γHH – V) was calculated from the correlation between HH and VV polarization from pre- and post-event PolSAR data. In this study, we deal with the detection of landslides using pre- and post-event polarimetric parameters from PolSAR data. The largest landslide area in Minami-Aso village was clearly showed the surface scattering because the landslide induced by earthquake removed forested vegetation on the ground surface. The extent of the landslides was detected using pre- and post-event PolSAR data with Random forest (RF) classifier. It is clarified that pre- and post-event alpha angle, entropy and γHH – V from PolSAR data with the RF classifier is effective for landslide detection.