Vision Character based Multi-Sensor Image Registration
基于视觉特征的多传感器图像配准

Multi-sensor image registration is important for the spatial data processing, which especially faces the isomerism problems. In multi-modal image registration, the parameters of the transformation between the two images could be estimated by the corresponding pairs of control points. Facing with the difficulties of multi-modal images, this registration algorithm adopts the vision characters and multi-resolution based feature matching to carry out the new coarse-to-fine registration. It is composed of two steps. The first step is to set up wavelet-based image pyramid to decompose images into extract base contours and detail contours. In this step, an extended contour searching algorithm is developed to settle the image noises. The second step is to design a minimum distance classification based multi-resolution shape matching algorithm on the Fourier curvature representation of the chain code contour. Transformation parameters are estimated based on the final matched control-point pairs which are the centers of gravity of the closed contours. The multi-sensor Landsat TM imagery and infrared imagery have been used as experimental data to compare the algorithm with the classical contour-based registration. Experimental results show that this registration is superior to the classical ones.