Geometric correction method to correct the influence of attitude jitter on remote sensing imagery using compressive sampling

Abstract

Attitude jitter occurs widely in the applications of high-resolution satellites. It is a vital factor that deteriorates the accuracy of geopositioning and mapping. However, the normal geometric correction methods cannot eliminate the influence of jitter on remote sensing images. Therefore, it is important to design a method that can handle this problem. This paper presents a geometric correction method using a rational function model (RFM) and compressive sampling called RFM-CS. This method is divided into two modules: precorrection and compensation. In the precorrection part, the original image is geometrically corrected with an RFM. However, when raw images contain distortion caused by attitude jitter, the rational polynomial coefficients (RPCs) don’t approximate the real imaging process well, and there are significant residual distortions in precorrected images. In the compensation part, we propose a new idea by which the residual distortions of images can be expressed as two-dimensional signals, which are called distortion signals. Using the new idea and CS, distortion signals, even those containing the influence of attitude jitter, are exactly reconstructed with a small set of ground control points. Based on the reconstructed distortion signals, the residual geometric error in the remote sensing images can be compensated by resampling. The experiments with images from Advanced Spaceborne Thermal Emission and Reflection Radiometer, Advanced Land Observing Satellite, and simulation demonstrate the promising performance and feasibility of the proposed method.