Estimating the pixel footprint distribution for image fusion by ray tracing lines of sight in a Monte Carlo scheme

Images from airborne cameras can be a valuable resource for data fusion, but this typically requires them to be georeferenced. This usually implies that the information of every pixel should be accompanied by a single geographical position describing where the center of the pixel is located in the scene. This geospatial information is well suited for tasks like target positioning and orthorectification. But when it comes to fusion, a detailed description of the area on the ground contributing to the pixel signal would be preferable over a single position. In this paper we present a method for estimating these regions. Simple Monte Carlo simulations are used to combine the influences of the main geometrical aspects of the imaging process, such as the point spread function, the camera’s motion and the topography in the scene. Since estimates of the camera motion are uncertain to some degree, this is incorporated in the simulations as well. For every simulation, a pixel’s sampling point in the scene is estimated by intersecting a randomly sampled line of sight with a 3D-model of the scene. Based on the results of numerous simulations, the pixel’s sampling region can be represented by a suitable probability distribution. This will be referred to as the pixel’s footprint distribution (PFD). We present results for high resolution hyperspectral pushbroom images of an urban scene.