Registration of high-dimensional remote sensing data based on a new dimensionality reduction rule

Registration of remote sensing data often involves dimensionality reduction of high-dimensional data to yield an image from each data set followed by pairwise image registration. We develop a new rule for dimensionality reduction such that the the Cramér-Rao lower bound (CRLB) for the estimation of the transformation parameters is minimized. A hyperspectral data set and a multispectral data set are used to evaluate our proposed rule. The experimental results using Mutual Information (MI) based pairwise registration technique demonstrate that our proposed rule can select the image pair with more texture, resulting in improved image registration results.