A Point Matching Research For Remote Sensing Image Based On The Best Geometric Constraint

As the prerequisite for image correction,image mosaic,image fusion,target recognition and so on,image registration is one of the most basic and widely used technologies in digital image processing,and has been widely concerned by domestic and international scholars.It plays an Irreplaceable role in a number of fields such as military affairs,medical treatment,industrial production,aerospace and people’s daily life.Based on the feature point matching technology,the basic principles and flow of image registration algorithm and the characteristics of various algorithms were discussed and summarized in detail.To solve the lack of the global relevance of matching point sets in the process of traditional matching algorithm,a point matching algorithm based on the best geometric constraint was implemented.The algorithm was the improvement of the Scale Invariant Feature Transform(SIFT)matching algorithm.Firstly,a transformation model was built to describe the feature point sets in images.Then a modified quantum particle swarm optimization(QPSO)was utilized to optimize the parameters of the model.Each time after the position of particles were updated,a feature point matching algorithm on the basis of search circle was used to obtain the matching points in the new position,and the suitability degree and the auxiliary suitability degree of particles being calculated to evaluate the position of the particles.After several iterations,the optimal geometric constraint of the matching image and the corresponding matching points were acquired,which realized the matching of feature points.In experiment part,several remote sensing images were adopted for point matching.the experiment results demonstrate that the algorithm in this paper is robust to image rotation,luminance and scale change.the number of matching points is improved about 1.5 times higher than that of the SIFT algorithm,and the accuracy of matching results is improved about 10%,Comparing with the SURF matching algorithm,which shows that the algorithm is capable of obtaining better point matching results both in quantity and accuracy.