Statistical Methods in Photogrammetry and Image-Lidar Fusion
A primary application of photogrammetry is to process, discriminate and classify optical imagery into 3D objects. Light detection and ranging (lidar) is an active sensor that measures the surfaces of 3D objects as discrete points in a point cloud. From the perspective of 3D objects in a common scene, point clouds and photogrammetry are related. Through this relationship there are numerous photogrammetric applications of lidar.
This dissertation is concerned with three specific applications of lidar: modeling radiometric properties as a basis for comparison with imagery, estimating camera pose and data fusion. It is partial to the problems of object discrimination and classification, and presents solutions in a statistical context.
A reflectance image is derived from reflectance, shadow and projection models. The reflectance image model is applied to compare the point cloud and imagery. The collinear equations of imaging are reparameterized as an object to image space transformation and estimated using maximum likelihood. Reflectance images are applied to quantify errors in this transformation across multiple images and to study the convergence properties of estimates. Finally, the process of image-lidar fusion is discussed in the context of uncertainty and probability. An estimator is specified for image-lidar fusion, derived from a generalized theory of the process. The estimator is shown to be unbiased and relatively efficient compared to the sample mean.