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Published April 1993 | public
Journal Article

Development of an Improved Image Processing Based Visibility Model

Abstract

An image processing based visibility model is presented in which model calculations are displayed as synthetic color photographs that have the appearance of a smog event. A theoretically based model for light scattering and absorption in a plane parallel atmosphere is used to determine sky color and to provide accurate prediction of skylight addition to the line of sight between the observer and objects in the field of view. Data for testing the visibility model are collected experimentally, including standard photographs of chosen vistas and measurements of the chemical composition and size distributions of the atmospheric aerosol. The model is verified by comparison against radiometer measurements as well as point by point comparison of actual photographs and synthetic images. It is shown that the image processing based visibility model performs well, producing representations of the sky color and objects in the field of view that are close to those seen in actual photographs.

Additional Information

© 1993 American Chemical Society. Received for review March 18, 1992. Revised manuscript received September 24, 1992. Accepted October 26, 1992. We thank Christine Sloane of General Motors Research Laboratories for advice and assistance on the skylight model calculations. Radiometer measurements were performed by Luis Matamala, and Luiz Palma assisted with the experiments. We thank Ron Henry for making the radiometer available for this work. Analysis of filter samples was conducted by Lynn Salmon (ion chromatography), John Cooper (XRF), and Bob Cary (carbon analysis). NO_2 concentrations and mixing depth information were provided by the South Coast Air Quality Management District. Financial support for this research was provided by the Caltech Center for Air Quality Analysis and by the National Aeronautics and Space Administration.

Additional details

Created:
August 20, 2023
Modified:
October 19, 2023