- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
Compared with optical sensors, Synthetic Aperture Radar (SAR) can provide important damage information due to its ability to map areas affected by earthquakes independently from weather conditions and solar illumination. In 2013, a new TerraSAR-X mode named staring spotlight (ST), whose azimuth resolution was improved to 0.24 m, was introduced for various applications. This data source made it possible to extract detailed information from individual buildings. In this paper, we present a new concept for individual building damage assessment using a post-event sub-meter very high resolution (VHR) SAR image and a building footprint map. With the building footprint map, the original footprints of buildings can be located in the SAR image. Based on the building imaging analysis of a building in the SAR image, the features in the building footprint can be extracted to identify standing and collapsed buildings. Three machine learning classifiers, including random forest (RF), support vector machine (SVM) and K-nearest neighbor (K-NN), are used in the experiments. The results show that the proposed method can obtain good overall accuracy, which is above 80% with the three classifiers. The efficiency of the proposed method is demonstrated based on samples of buildings using descending and ascending sub-meter VHR ST images, which were all acquired from the same area in old Beichuan County, China.
Damage detection after earthquakes is an important issue for post-disaster emergency response, impact assessment and relief activities. Building damage detection is particularly crucial for identifying areas that require urgent rescue efforts. Remote sensing has shown excellent capability for use in rapid impact assessments, as it can provide information for damage mapping in large areas and in an uncensored manner, particularly when information networks are inoperative and road connections are destroyed in areas impacted by earthquakes. Compared with optical sensors, Synthetic Aperture Radar (SAR) can provide important damage information due to its ability to map affected areas independently from the weather conditions and solar illumination, representing an import data source for damage assessment.
In this paper, we present a new damage assessment method for buildings using single post-earthquake sub-meter resolution VHR SAR images and original building footprint maps. The method can work at the individual building level and determines whether a building is destroyed after an earthquake or is still standing. First, a building footprint map covering the study area is obtained as prior knowledge. Then, an SAR image is geometrically rectified by the ground control points provided by the SAR product files. After rectification, the SAR image can be registered as the building footprint map. Thus, with the building footprint map, the original footprint of a building can be located in the SAR image. Then, features can be extracted in the image patch of a building’s footprint to form a feature vector. Finally, the buildings can be classified into damage classes with classifiers.