Improved Parameter Estimation of the Line-Based Transformation Model for Remote Sensing Image Registration

Abstract

Abstract:The line-based transformation model (LBTM), built upon the use of affine transformation, was previously proposed for image registration and image rectification. The original LBTM first utilizes the control line features to estimate six rotation and scale parameters and subsequently uses the control point(s) to retrieve the remaining two translation parameters. Such a mechanism may accumulate the error of the six rotation and scale parameters toward the two translation parameters. In this study, we propose the incorporation of a direct method to estimate all eight transformation parameters of LBTM simultaneously using least-squares adjustment. The improved LBTM method was compared with the original LBTM through using one synthetic dataset and three experimental datasets for satellite image 2D registration and 3D rectification. The experimental results demonstrated that the improved LBTM converges to a steady solution with two to three ground control points (GCPs) and five ground control lines (GCLs), whereas the original LBTM requires at least 10 GCLs to yield a stable solution.

Multispectral Lidar-imagery Data Fusion and Multilevel Classification of Lidar Point Clouds for Urban Features Extraction

“…However, the direct identification of common points on 3D LiDAR data is not recommended, as they represent laser footprints rather than identifiable points in corresponding imagery data. Primitives other than points (i.e., lines, curves, and polygons) may be used in case the nature of the study scene does not allow for sharp and permanent control points [11,26,27].…”

“…Remote sensing image registration relies on the use of common control points being identified between the target and reference datasets to construct a coordinate transformation model [24,25]. If there exists a lack of sharp and permanent control points due to the nature of the study scene, other registration primitives, including lines, curves, and polygons, can also be utilized [11,26,27]. Researchers abandoned the direct identification of common points when dealing with LiDAR data since they represent laser footprints rather than identifiable points in corresponding imagery data.…”