SAR image registration method based on deep neural networks

Abstract

The invention provides a SAR image registration method based on deep neural networks, and the method comprises the realization steps: (1) a training sample is acquired; (2) the deep neural networks are designed and trained; (3) matching relations are predicted; (4) wrong matching points are removed; (5) a geometric transformation parameter is calculated; and (6) the image is registrated. According to the invention, the SAR image registration method based on deep neural networks is adopted, the artificial design feature extraction operator defects including high complexity, large amount of computation, poor robustness in a process and the like in prior art can be effectively overcome, the amount of computation can be effectively reduced, the deep neural networks can extract substantive characteristics of the image, the robustness is better than before, and the precision of the registration is higher than before.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a synthetic aperture radar (synthetic aperture radar, SAR) image registration method based on a deep neural network. The invention can be applied to the fields of computer vision, remote sensing image processing, pattern recognition and the like for image fusion, change detection, target detection and recognition, and the like. Background technique [0002] Synthetic aperture radar is a high-resolution active microwave remote sensing imaging radar, which has the advantages of all-weather, all-weather, high resolution, and side-view imaging. It can be used in many fields such as military, agriculture, navigation, and geographical surveillance. . SAR image registration can be used for change detection, object detection and recognition. [0003] The existing image registration methods are mainly divided into grayscale-based registration methods and feature-bas...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that the artificially designed Harris, SIFT, and SURF methods to extract features have a large amount of calculation, and the Harris method is unstable when extracting feature points. In SAR images, SIFT and SURF methods are easy to mistake noise points as Feature points, which affect the accuracy of registration

The shortcomings of this method are: First, the existing artificially designed line detection methods in SAR images are not effective, which affects the detection of characteristic lines, and the prior information is insufficient, which reduces the accuracy of image registration

Second, the method of registration based only on features is not reliable

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