Orthorectification of Sich-2 satellite images using elastic models

In this paper, a new method for automatic identification of ground control points (GCPs) on optical remote sensing images is presented. An elastic Radial Basis Function (RBF) neural network based model for nonlinear coordinate transformation and image rectification is proposed. The new method can be used to produce dense fields of about thousands of GCPs per image to train highly deformable transformation models. As a result, an accuracy improvement of order of 4 in comparison with the Automated Precise Orthorectification Package (AROP) can be obtained. The proposed method is applied for the Ukrainian remote sensing satellite Sich-2. The obtained average RMSE error by the new method for Sich-2 images is estimated at 17.8 m.