KEEPING THE BENEFIT OF ILMD’s HIGH RESOLUTION IN MEASURING LIGHTING QUALITY PARAMETERS

The dimensioning of lighting installations is performed according to the specifications of CIE documents and relevant standards. Differences are often observed between simulations and experimental measurements regarding the quality criteria and luminance distributions. This paper deals with road lighting quality parameters evaluation between experimental ILMD images and calculations. Influence of image rectification is first examined. Impacts of image resolution, grid resolution and grid positioning on average luminance, overall uniformity and longitudinal uniformity are evaluated. An elliptical method based on human eye resolution and perspective projection is presented for road lighting calculations. It conducts to a good robustness regarding misalignment and give access to a pixel-to-pixel comparison with experimental ILMD images.

Improved parameter estimation of the line-based transformation model for remote sensing image registration

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. Keywords: image registration; image rectification; remote sensing; ground control lines; line-based transformation model

Improved parameter estimation of the line-based transformation model for remote sensing image registration

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. Keywords: image registration; image rectification; remote sensing; ground control lines; line-based transformation model