%0 Journal Article
%T Optical Flow Inversion for Remote Sensing Image Dense Registration and Sensor's Attitude Motion High-Accurate Measurement
%A Chong Wang
%A Zheng You
%A Fei Xing
%A Borui Zhao
%A Bin Li
%A Gaofei Zhang
%A Qingchang Tao
%J Mathematical Problems in Engineering
%D 2014
%I Hindawi Publishing Corporation
%R 10.1155/2014/432613
%X It has been discovered that image motions and optical flows usually become much more nonlinear and anisotropic in space-borne cameras with large field of view, especially when perturbations or jitters exist. The phenomenon arises from the fact that the attitude motion greatly affects the image of the three-dimensional planet. In this paper, utilizing the characteristics, an optical flow inversion method is proposed to treat high-accurate remote sensor attitude motion measurement. The principle of the new method is that angular velocities can be measured precisely by means of rebuilding some nonuniform optical flows. Firstly, to determine the relative displacements and deformations between the overlapped images captured by different detectors is the primary process of the method. A novel dense subpixel image registration approach is developed towards this goal. Based on that, optical flow can be rebuilt and high-accurate attitude measurements are successfully fulfilled. In the experiment, a remote sensor and its original photographs are investigated, and the results validate that the method is highly reliable and highly accurate in a broad frequency band. 1. Introduction For the remote sensors in dynamic imaging, one important technology is image motion compensation. Actually, to determine image motion velocity precisely is a very hard problem. In [1, 2], optical correlators are utilized to measure image motion in real time based on a sequence of mild smeared images with low exposure. This technique is appropriate to the situations in which the whole image velocity field is uniform. Some other blind motion estimation algorithms in [3¨C5] have been used to image postprocessing, which can roughly detect inhomogeneous image motion, but lack real-time performance because of complexity. As for space imaging, in order to avoid motion blurring, image motion velocity needs to be computed in real time according to the current physical information about spacecraft¡¯s orbit and attitude motion, which can be obtained by the space-borne sensors, such as star trackers, gyroscopes, and GPS. Wang et al. developed a computational model for image motion vectors and presented error budget analysis in [6]. They focused on the small field of view (FOV) space cameras which are used in push-broom imaging with small attitude angles. In that situation, the nonlinearity of image motion velocity field does not appear significantly. However, for others with larger FOV, image motion velocity fields are definitely nonlinear and anisotropic because the geometry of the planet will
%U http://www.hindawi.com/journals/mpe/2014/432613/