The Buildings Earthquake Damage Analysis Based On LiDAR

Light Detection and Ranging(Li DAR) is a new type of technology developed in recent years in the field of photogrammetry and remote sensing. Its application in post-earthquake assessment and emergency rescue is still in the early stage of exploration, but it has already shown great potential. From the demand earthquake emergency and rapid assessment using remote sensing methods, focusing on the application of new remote sensing technology, basing on ground-based LiDAR, we deeply study the point cloud data acquisition and processing methods of the quake-damaged buildings. On this basis, the earthquake damage extraction and analysis methods are researched by using point cloud data. The main works of this paper are summarized as follows:1. The key technology of carrying out measurements using ground-based Li DAR is studied. The obtained point cloud data is irregularity, high data redundancy and noisy. To address this problem, through improving algorithm parameters,the threshold are determined on the reasonable balance between efficiency and effectiveness of data processing, and finally test the validity and usefulness of the two algorithms used in combination. Through theoretical analysis, data processing experiment and field verification, we realize the effective processing of damaged buildings point clouds.2. Using three-dimensional point cloud data and convex hull algorithm, K-Means clustering and discriminant analysis, the TLS-BSAM model is proposed based on the shape analysis theory and solves the building high polygon sequence extraction, shape discrete parameter extraction, irregular buildings block segmentation, damage analysis and other issues. When using two-dimensional contour polygon sequence to depict a three-dimensional building, 0.5-1m high polygon is a reasonable sampling interval to carry out the analysis in terms of damage.Taking length-width ratio of the polygon r, the inclination direction θ, rectangle R, compactness C and the center point(x, y) as the characteristic parameters to carry out K-Means clustering can efficiently divide irregular buildings into blocks. Furthermore, by weighted average, shape discrete parameter can be extracted to unified express the shape characteristics of single building.Among the shape discrete parameters,inclination direction, rectangularity,compactness and center can at least reflect buildings destruction and the higher the value, the higher the degree of damage.Under the existing data conditions, this model can effectively identify the damage degree.3.Based on the object-oriented method, we introduce height echos and echo intensity as the features.This enriches the traditional building character description factor. Combiened with the spectral characteristics of point cloud, the rule set of buidlings extraction on point cloud data are set up and relize the building information extraction in high accuracy. Compared with the field survey results, extraction accuracy can reach above 90%.4. The point cloud data and SAR image are combined to analyze. Based on the DEM and three-dimensional model of damage buildings, using Range-Doppler(R-D) and ray tracing method, we carry out SAR image simulation and compare with real SAR images. Through the comparation with real SAR image, it is conducive to complex image interpretation and change detection and will support the analysis and understanding of damaged buildings in SAR images.This provides a new idea and approach for SAR images interpretation and building damage analysis after earthquake.