In this paper, we describe Optical Aperture Synthesis (OAS) imaging instrument concepts studied by Alcatel Alenia Space under a CNES R&T contract in term of technical feasibility. First, the methodology to select the aperture configuration is proposed, based on the definition and quantification of image quality criteria adapted to an OAS instrument for direct imaging of extended objects. The following section presents, for each interferometer type (Michelson and Fizeau), the corresponding optical configurations compatible with a large field of view from GEO orbit. These optical concepts take into account the constraints imposed by the foreseen resolution and the implementation of the co-phasing functions. The fourth section is dedicated to the analysis of the co-phasing methodologies, from the configuration deployment to the fine stabilization during observation. Finally, we present a trade-off analysis allowing to select the concept wrt mission specification and constraints related to instrument accommodation under launcher shroud and in-orbit deployment.
The use of laser technology to scan hardwood log surfaces for defects holds great promise for improving processing efficiency and the value and volume of lumber produced. External and internal defect detection to optimize hardwood log and lumber processing is one of the top four technological needs in the nation's hardwood industry. The location, type, and severity of defects on hardwood logs are the key indicators of log quality and value. These visual cues provide information about internal log characteristics and products for which the log is suitable. We scanned 162 logs with a high-resolution industrial four-head laser surface scanner. The resulting data sets contain hundreds of thousands of three-dimensional coordinate points. The size of the data and noise presented special problems during processing. Robust regression models were used to fit geometric shapes to the data. The estimated orthogonal distances between the fitted model and the log surface are converted to a two-dimensional image to facilitate defect detection. Using robust regression methods and standard image processing tools we have demonstrated that severe surface defects on hardwood logs can be detected using height and contour analyses of three-dimensional laser scan data.
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