Surface Deformation Analysis by Fusion of Multi-temporal and Multi-source Data under Disaster Emergency Conditions

Abstract

Rapid analysis of surface deformation is crucial for rescue operations following natural disasters. However, the lack of recent terrain data and the discrepancy between data types and field-collected data often hinder timely surface deformation analysis. To enhance data usability, this paper proposes an analytical method that integrates multiple sources of remote sensing data, including satellite data, oblique photography data, and LiDAR data. By merging oblique images with grayscale point clouds, true-color point clouds are generated. The method optimizes old data through threshold segmentation and median filtering, then converts and unifies the resolution of multi-source data via data interpolation. Elevation interpolation matrices are employed for overlay analysis, and a combination of bilateral filtering and threshold processing is used. This groundbreaking approach enables the completion of surface deformation analysis in emergency geospatial surveys and has been validated in various typical regions, demonstrating its application potential across different surface environments. Experimental results indicate that this method can quickly utilize multi-temporal and multi-source remote sensing data to effectively identify surface deformations following natural disasters.