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Multilevel Wavelet Decomposition Based Harris Corner Detection Algorithm for Remote-sensing Image

Because of the interference of the noise and the subtle texture, the Harris algorithm will generate much more false corners. An improved Harris algorithm based on multilevel wavelet decomposition is proposed. By multilevel wavelet decomposition, the high frequency components of image are extracted. These high frequency components with different scales are parsed, and the noises and subtle textures are suppressed while the original image edge features are kept. The final experiment shows that the improved corner detection algorithm can effectively reduce the number of false corners. Introduction The geometric feature extraction is a major task for remote-sensing data registration. The key to the extraction is the feature point matching between images. Harris algorithm is a widely used corner detection algorithm in feature extraction because of its simple calculation and high stability. However, in the process of extracting, corners are vulnerable to the interference of noise and subtle texture, which causes more false corners. In order to resolve this problem, an improved Harris algorithm based on multilevel wavelet decomposition is proposed in this paper. Though the decomposition, the subtle texture and noise are suppressed and corner detection results are optimized. It is verified by experiment that this improved corner detection algorithm can obviously improve the quality of corner detection and reduce the number of false corner points. This paper has five sections. The second section introduces the related research work of corner detection. In the third section, based on the analysis of Harris algorithm, an improved method based on multilevel wavelet decomposition is proposed. The fourth section is the experiment. The last section is the conclusion. Related work Feature-based image registration algorithms are widely studied [1],[2] . By comparing the feature sets with obvious gray scale changing, this kind of algorithm uses feature matching algorithm to find related feature points. And then the mapping relation is established according to the feature points to achieve the goal of image registration. According to the models of the feature points, feature-based image registration algorithms can be divided into three categories: edge point based [3] , corner based [4] and interest operator based [5] . Corner is a kind of feature points that reflects the local extremum of curvature on contour. Compared with other types of feature points, corner can represent the features of the ground surface much better [6],[7] .In 1981, Moravec proposed the idea of using the corners of local area on image for stereo matching. As the first widely used corner detection algorithm, this algorithm laid a foundation for theoretical research of corner detection [8] . In 1988, Harris improved Morvec's corner detection algorithm and enhanced the robustness of corners [9] .