Rapid matching method of multispectral images based on edge detection

Abstract

The invention discloses a rapid matching method of multispectral images based on edge detection, which comprises the following steps of: (1) acquiring multispectral grayscale images of the same scene at any angle of vision; (2) enhancing the multispectral grayscale images and filtering the images to denoise; (3) extracting the edges of the denoised multispectral grayscale images; (4) filtering the edges of the multispectral grayscale images by using a square window with fixed size to acquire a discrete dot chart of the edges; and (5) setting solved matching parameters, optimally calculating the matching parameters by using the particle swarm optimization method to acquire optimal matching parameters. The matching method disclosed by the invention does not have any requirement on the angle of vision and a lens of a detector while acquiring the multispectral images and has the characteristics of high matching speed, strong robustness and strong fault tolerance.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an image matching method. [0002] technical background [0003] Image matching refers to identifying points with the same name between two or more images through a certain matching algorithm, and its essence is the optimal search problem using matching criteria under the condition of primitive similarity. Image matching can be mainly divided into grayscale-based matching and feature-based matching. Matching methods based on grayscale include cross-correlation registration method, Fourier method and maximum mutual information method and so on. The feature-based matching method is to extract the features of the image, including feature points, target edges, and terrain feature lines (such as ridge-valley lines, rivers, roads, room angles), etc., and use these features to calculate spatial transformation parameters. These matching methods are not suitable for all image ...

AI Technical Summary

Problems solved by technology

The disadvantages of this method are: 1. The matching effect is poor. Although the coaxial lens is used, the field of view cannot be completely consistent under normal circumstances. In addition, the resolution of different detectors is different, resulting in the effect of this matching method. Very poor; 2. The requirements for the detector lens are high. The detector lens used in this matching method is usually fixed. If the lens is replaced, the matching cannot be achieved; 3. The robustness is poor, and it can only be used for a specific 4. Poor fault tolerance. This method has high requirements on the image quality of multispectral images. If the image is slightly deformed, there will be a phenomenon that it cannot be matched.

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