A Detection Method for Collapsed Buildings Combining Post-Earthquake High-Resolution Optical and Synthetic Aperture Radar Images

Abstract

The detection of collapsed buildings based on post-earthquake remote sensing images is conducive to eliminating the dependence on pre-earthquake data, which is of great significance to carry out emergency response in time. The difficulties in obtaining or lack of elevation information, as strong evidence to determine whether buildings collapse or not, is the main challenge in the practical application of this method. On the one hand, the introduction of double bounce features in synthetic aperture radar (SAR) images are helpful to judge whether buildings collapse or not. On the other hand, because SAR images are limited by imaging mechanisms, it is necessary to introduce spatial details in optical images as supplements in the detection of collapsed buildings. Therefore, a detection method for collapsed buildings combining post-earthquake high-resolution optical and SAR images was proposed by mining complementary information between traditional visual features and double bounce features from multi-source data. In this method, a strategy of optical and SAR object set extraction based on an inscribed center (OpticalandSAR-ObjectsExtraction) was first put forward to extract a unified optical-SAR object set. Based on this, a quantitative representation of collapse semantic knowledge in double bounce (DoubleBounceCollapseSemantic) was designed to bridge a semantic gap between double bounce and collapse features of buildings. Ultimately, the final detection results were obtained based on the improved active learning support vector machines (SVMs). The multi-group experimental results of post-earthquake multi-source images show that the overall accuracy (OA) and the detection accuracy for collapsed buildings (Pcb) of the proposed method can reach more than 82.39% and 75.47%. Therefore, the proposed method is significantly superior to many advanced methods for comparison.

BD-SKUNet: Selective-Kernel UNets for Building Damage Assessment in High-Resolution Satellite Images

“…Even in high-resolution images, it was difficult to understand the details of the damages from close-nadir-looking satellite images [29,47]. Therefore, based on the previous studies [48,[62][63][64][65], and since the visual interpretation of pre-and post-disaster images was complicated for multi-class labeling, we only considered two classes of damage for further steps. Additionally, the building vector map was employed to locate the buildings and decrease human error during interpretation.…”

Building Change Detection Based on a Gray-Level Co-Occurrence Matrix and Artificial Neural Networks

“…In other research, the authors detected collapsed buildings using three different textural features derived from the GLCM and applying an SVM classifier [51]. The SVM and a synergy of high-resolution optical and Synthetic Aperture Radar (SAR) images were also used for the detection of the collapsed buildings after the 2011 Japan earthquake [52]. Finally, in [41], the authors detected collapsed buildings after the 2017 Iran-Iraq earthquake using ten spectral indices in combination with seven different textural features derived from the GLCM and applying an SVM classifier.…”

Extraction of Earthquake Damage Information and Mapping of Buildings from Single Post-earthquake Polarimetric Synthetic Aperture Radar Image Based on Polarimetric Decomposition and Texture Features

“…In this study, we only use the single-temporal post-earthquake SAR data to identify the degree of damage of buildings in disaster areas, whereby we can avoid the multitemporal data registration operation. (10) Fourpolarimetric SAR (PolSAR) data contain more information than remote sensing data of a singlepolarimetric or dual-polarimetric radar, because PolSAR data comprise four polarimetric channels: HH, HV, VH, and VV, where H represents horizontal polarization and V represents vertical polarization. In cases where only the single post-earthquake SAR data can be used for assessing post-earthquake building damage, we select to use PolSAR data to achieve a higher accuracy of damage identification and a more reliable post-earthquake damage assessment.…”