Autonomous registration of disparate spatial data via an evolutionary algorithm toolbox

Abstract

In this paper, we present the registration of disparate spatial data. To be specific, we consider the registration of digital terrain elevation data (DTED) to National High Altitude Photography (NHAP). Initially, the DTED is shaded to form a synthetic image, and our registration process maps point in the shaded image to points in the NHAP. For the purpose of comparison, we propose two distinct techniques for matching. The first method is a semi-autonomous. It requires two pairs of user defined matched points to estimate an initial transform as starting point in the search for the best fitting transform using Nelder-Mead Simplex Method. The second method, being more novel in nature, attempts to eliminate the need for any user intervention and registers the two data autonomously by employing the Multi Objective Evolutionary Algorithm (MOEA) toolbox. Both methods worked well in estimating the best fitting affine transform to register the image and elevation data, and the MOEA based autonomous technique outperforms the much simpler single objective based semi autonomous technique.