An application of an advanced stochastic bottom imaging model for airborne hyperspectral imager data collection

Collection of airborne data can be an expensive exercise. Data flights should optimize the quality and quantity of the data collected at minimal cost. Although the site to be surveyed is fixed, a mission planner has some freedom in formulating collection strategies. Choices may include the season. the time of day, the altitude and directions of the data run flights, the spectral bands, and the spectral and spatial resolutions used for the survey. A stochastic model has been developed to simulate and quantitatively estimate the statistical performance of airborne hyper- and multi-spectral systems in imaging a littoral sea bottom through a wavy sea surface. Results include mean and variance of various measures of system performance. Candidate collection plans can be tested with the stochastic model. This paper demonstrates the use of the stochastic model in examining the effect of flight direction on the quality of imagery for a variety of zenith sun angles and surface wave conditions. The calculations show the extreme sensitivity of data quality in terms of image signal to noise ratios to flight direction, sun angle (time of day and latitude), and sea wave direction.