A Machine Learning Approach to Georeferencing

Abstract

Imaging from space involves certain complications which are quite different from airborne platforms such as MAVs, UAVs and drones. All these platforms require mathematical models to represent the geometry of image acquisition and further georeferencing the acquired image. Conventionally, a Rigorous Sensor Model (RSM) involving mission critical parameters and a sequence of rotations serves the purpose, alternately Rational Functional Models (RFM) are developed which empirically mimics RSM to certain degree of acceptable accuracy. In this paper, a machine learning approach is proposed for georeferencing of satellite images and compares the results with RFM and RSM.