Photogrammetric Model-based Registration
and Fusion of Multiple Types of Passive Imagery
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Image registration is a broad topic which is not only applied to photogrammetry and remote sensing problems, but also to medical imaging, pattern recognition, and computer vision activities. Image registration can be categorized into non-photogrammetric (or non sensor model-based) and photogrammetric (or sensor model-based). Registration problems can also be classified into 2D-to-2D, 3D-to-2D, and 3D-to-3D. In this book, the focus is on 2D-to-2D and 3D-to-2D image registration. The primary thrust of this research is in three main areas: replacement sensor model, epipolar image rectification, a...
Image registration is a broad topic which is not only applied to photogrammetry and remote sensing problems, but also to medical imaging, pattern recognition, and computer vision activities. Image registration can be categorized into non-photogrammetric (or non sensor model-based) and photogrammetric (or sensor model-based). Registration problems can also be classified into 2D-to-2D, 3D-to-2D, and 3D-to-3D. In this book, the focus is on 2D-to-2D and 3D-to-2D image registration. The primary thrust of this research is in three main areas: replacement sensor model, epipolar image rectification, and image registration. Several types of image acquisition datasets, from aerial frame, airborne whiskbroom, and satellite pushbroom (linear array scanners) passive sensors, are used. A novel approach, termed generalized True Replacement Model (TRM), is developed to satisfy the following characteristics: highly accurate ground-to-image function, rigorous error propagation, and an ability for model parameter refinement. More importantly, the generalized TRM can perform relative triangulation without any use of ground control points (GCPs) in a manner similar to the original model.
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