Georeferencing Remote Sensing Data Using Long Gradients

Abstract

The paper investigates algorithms using long intensity gradients for georeferencing of Earth remote sensing data. The case is considered in which one “reliable” referenced set of remote sensing data is already known for a particular area. New input data are referenced to this “reliable” set by detecting resemblant fragments in the “relible” data set and new remote sensing data. A set of pairs of resemblant fragments makes it possible to calculate the transformation parameters of new data. To increase the efficiency of resemblant fragments detection, we go to the space of long intensity gradients, which makes the georeferencing method more stable to admissible differences between resemblant fragments. The paper considers a few algorithms of going to the long gradient space and compares them. The computaional experiment provides grounds for recommending the best way of going to the long gradient space.