The RANSAC (random sampling consensus) algorithm is an estimation method that can obtain the optimal model in samples containing a lot of abnormal data. This algorithm uses a small number of points in the data to estimate the model, and then uses the remaining points to check the model. It is now widely used in computer vision image stitching. This algorithm has disadvantages such as slow operation speed and poor sample adaptability. In order to improve the efficiency of image registration, many researchers have made improvements on the basis of the RANSAC principle. This article introduces the principles and shortcomings of RANSAC, and introduces four ways to improve it in view of its shortcomings. The advantages and problems of the improved algorithm are analyzed to provide a basis for the field of image registration.
Remote sensing image change detection is the detection process of determining the surface change area and change feature type for the same image area from multiple time series remote sensing data. It is the core technical means of land use change detection and land cover change detection, and it is also the key research field of remote sensing applied science. In view of the problems of time-consuming, labor-intensive, and low detection accuracy in the past image change detection methods, researchers in related fields have proposed more and more cutting-edge remote sensing image change detection methods. This article first describes the development of remote sensing image change detection methods, then explains the conventional processing flow of change detection and the conventional methods of change monitoring, and finally discusses the research progress of more mainstream change detection methods.
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