AERIAL IMAGES AND LIDAR DATA FUSION FOR DISASTER CHANGE DETECTION
- 1School of Surveying and Spatial Information Systems, The University of New South Wales, UNSW SYDNEY NSW 2052, Australia
- 2Dept. of Surveying, Faculty of Engineering Shoubra, Benha University, 108 Shoubra Street, Cairo, Egypt
Keywords: LiDAR, Aerial Images, Change detection, Building extraction, Feature extraction.
Abstract. Potential applications of airborne LiDAR for disaster monitoring include flood prediction and assessment, monitoring of the growth of volcanoes and assistance in the prediction of eruptions, assessment of crustal elevation changes due to earthquakes, and monitoring of structural damage after earthquakes. Change detection in buildings is an important task in the context of disaster monitoring, especially after earthquakes. Traditionally, change detection is usually done by using multi-temporal images through spectral analyses. This provides two-dimensional spectral information without including heights. This paper will describe the capability of aerial images and LiDAR data fusion for rapid change detection in elevations, and methods of assessment of damage in made-made structures. In order to detect and evaluate changes in buildings, LiDAR-derived DEMs and aerial images from two epochs were used, showing changes in urban buildings due to construction and demolition. The proposed modelling scheme comprises three steps, namely, data pre-processing, change detection, and validation. In the first step for data pre-processing, data registration was carried out based on the multi-source data. In the second step, changes were detected by combining change detection techniques such as image differencing (ID), principal components analysis (PCA), minimum noise fraction (MNF) and post-classification comparison (P-C) based on support vector machines (SVM), each of which performs differently, based on simple majority vote. In the third step and to meet the objectives, the detected changes were compared against reference data that was generated manually. The comparison is based on two criteria: overall accuracy; and commission and omission errors. The results showed that the average detection accuracies were: 78.9%, 81.4%, 82.7% and 82.8% for post-classification, image differencing, PCA and MNF respectively. On the other hand, the commission and omission errors of the results improved when the techniques were combined compared to the best single change detection method. The proposed combination of techniques gives a high accuracy of 92.2% for detection of changes in buildings. The results show that using LiDAR data in the detection process improves the accuracy of feature detection by 14.9% compared with using aerial photography alone.