Building Damage Estimation by Integration of Seismic Intensity Information and Satellite L-band SAR Imagery
نویسندگان
چکیده
For a quick and stable estimation of earthquake damaged buildings worldwide, using Phased Array type L-band Synthetic Aperture Radar (PALSAR) loaded on the Advanced Land Observing Satellite (ALOS) satellite, a model combining the usage of satellite synthetic aperture radar (SAR) imagery and Japan Meteorological Agency (JMA)-scale seismic intensity is proposed. In order to expand the existing C-band SAR based damage estimation model into L-band SAR, this paper rebuilds a likelihood function for severe damage ratio, on the basis of dataset from Japanese Earth Resource Satellite-1 (JERS-1)/SAR (L-band SAR) images observed during the 1995 Kobe earthquake and its detailed ground truth data. The model which integrates the fragility functions of building damage in terms of seismic intensity and the proposed likelihood function is then applied to PALSAR images taken over the areas affected by the 2007 earthquake in Pisco, Peru. The accuracy of the proposed damage estimation model is examined by comparing the results of the analyses with field investigations and/or interpretation of high-resolution satellite images.
منابع مشابه
Determining the extent of building destruction after an earthquake using satellite imagery and fuzzy logic (Case study of Sarpol-e-Zahab region)
Background and objective: Earthquake is one of the most destructive natural disasters. Earthquakes occur in urban areas, destroying buildings and injuring people living in them. Urban center buildings are among the features that are exposed to many hazards during an earthquake. One of the first measures taken after an earthquake is relief. Locating damaged buildings can speed up relief efforts....
متن کاملIntegration of Deep Learning Algorithms and Bilateral Filters with the Purpose of Building Extraction from Mono Optical Aerial Imagery
The problem of extracting the building from mono optical aerial imagery with high spatial resolution is always considered as an important challenge to prepare the maps. The goal of the current research is to take advantage of the semantic segmentation of mono optical aerial imagery to extract the building which is realized based on the combination of deep convolutional neural networks (DCNN) an...
متن کاملUsing dual-polarised L-band SAR and optical satellite imagery for land cover classification in Southern Vietnam: comparison and combination
Synthetic Aperture Radar (SAR) and optical images contain different types of land cover information. Moreover, whereas like-polarisation SAR images are predominately influenced by surface backscattering properties, the volume backscattering characteristics of surfaces are well represented by SAR cross-polarisation data. Hence, combined use of likeand cross-polarisation data could enhance the la...
متن کاملOn Surface Wind Speed Retrieval from Sar Imagery in West Pacific Ocean
With the development of climate change and global warming, more and more severe tropical storms, hurricanes and typhoons tend to emerge[nature], leading devastating threat and damage to human lives and social productions. Accurate forecasting of storm track and intensity is of vital importance to help evacuation and hence decrease losses. However, the accuracy of storm forecast heavily depends ...
متن کامل3D Classification of Urban Features Based on Integration of Structural and Spectral Information from UAV Imagery
Three-dimensional classification of urban features is one of the important tools for urban management and the basis of many analyzes in photogrammetry and remote sensing. Therefore, it is applied in many applications such as planning, urban management and disaster management. In this study, dense point clouds extracted from dense image matching is applied for classification in urban areas. Appl...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Remote Sensing
دوره 2 شماره
صفحات -
تاریخ انتشار 2010