Comparison of lidar and stereo photogrammetric point clouds for change detection

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Abstract

The advent of Light Detection and Ranging (LiDAR) point cloud collection has significantly improved the ability to model the world in precise, fine, three dimensional detail. The objective of this research was to demonstrate accurate, foundational methods for fusing LiDAR data and photogrammetric imagery and their potential for change detection. The scope of the project was to investigate optical image-to-LiDAR registration methods, focusing on dissimilar image types including high resolution aerial frame and WorldView-1 satellite and LiDAR with varying point densities. An innovative optical image-to-LiDAR data registration process was established. Comparison of stereo imagery point cloud data to the LiDAR point cloud using a 90% confidence interval highlighted changes that included small scale (< 50cm), sensor dependent change and large scale, new home construction change.

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