IDENTIFYING BUILDING CHANGE USING HIGH RESOLUTION POINT CLOUDS – AN OBJECT-BASED APPROACH
Keywords: LiDAR, point cloud, change detection, stereo, image analysis, remote sensing, LAS
Abstract. High resolution point clouds provide excellent data sources for examining change over time in above-ground features such as buildings and trees. Of particular interest is the identification of illegal construction activity or damage incurred during earthquakes and other disasters. By using multi-date point cloud layers, these types of change can be efficiently identified and mapped. Such analysis is generally not as simple as differencing imagery from the two dates. Variations between the images can be caused by slight geometric mismatches between images from different acquisition dates, errors in the data returns, or natural differences caused by vegetation growth or wind direction. The factors can contribute to the detection of large amounts of inconsequential change throughout the area of interest, resulting in too many false positives for the analysis to be of any practical use. However, by conducting object-based analysis of the data – analysing meaningful objects rather than working point by point – software algorithms can be used to rapidly and accurately detect and map only the changes of interest to the customer.