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.
Highlights
And accurate evaluation of earthquake damages to buildings after earthquakes is an important part of disaster surveillance [1]
The true ground map made through visual interpretation is regarded as a basis for accuracy evaluation, in which white, gray and black represent the collapsed buildings, non-collapsed buildings and others, respectively
Detectionresults results collapsed buildings based on Dataset (a) reference (b) proFigure ofof collapsed buildings based on Dataset
Summary
And accurate evaluation of earthquake damages to buildings after earthquakes is an important part of disaster surveillance [1]. Compared with traditional field survey methods, the remote sensing technology that adopts a remote imaging mode has many advantages, such as timely acquisition of information and not being limited by field conditions, so it has become the main technical means for extracting earthquake damage information of buildings [2,3]. The detection of buildings subjected to earthquake damages based on remote sensing images has mainly focused on the identification of collapsed buildings [4,5]. Introducing elevation information to traditional high-resolution remote sensing images can provide direct evidence support for judging whether buildings collapse or not. The acquisition of digital elevation data, such as light detection and ranging (LiDAR), usually requires extracting ground control points, with high computation complexity and time costs. It is difficult to meet the timeliness requirements for the detection of collapsed buildings after earthquakes [7,8]
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