Autonomous Ortho-Rectification of Very High Resolution Imagery Using SIFT and Genetic Algorithm

Abstract

Ortho-rectification of very high resolution imagery from agile platforms using Rigorous Sensor Model / Rational Functional Model is quite challenging and demands a fair amount of interactivity in Ground Control Point (gcp) identification/selection for refining the model and for final product evaluation. The paper proposes achieving complete automation in the ortho-rectification process by eliminating all the interactive components, and incorporating fault tolerance mechanisms within the model to make the process robust and reliable. The key aspects proposed in this paper are: two stage Scale Invariant Feature Transform (sift) based matching to obtain a large numbers of checkpoints using much coarser resolution images such as Landsat/etm+, followed by a ga to select the right combination of minimal gcps based on minimizing Root Mean Square Error (rmse) and maximizing the area covered under gcps, and finally, a decision rule based product evaluation to make the process operate in an “autonomous closed loop mode”. The method is generic and has been tested on hundreds of Cartosat-1/2 images, and has achieved above 90% reliability with sub-pixel relative error of reference data.