Aerial image rapid matching algorithm based on multi-characteristic Hash learning

Abstract

The invention discloses an aerial image rapid matching algorithm based on multi-characteristic Hash learning. The method is characterized by according to a course overlap rate of an aerial image, selecting a matched area, extracting a characteristic point in the matched area and acquiring a characteristic point set; carrying out multi-characteristic description on the acquired characteristic point so as to acquire a characteristic vector; through a nuclear method, mapping the characteristic vector to an uniform nuclear space; selecting training sample data, in the nuclear space, learning a binary system Hash code of a sample characteristic point and generating a Hash function; and according to the Hash function, carrying out binary system Hash code description on the characteristic point extracted from the matched area, and in a Hamming space, according to a Hamming distance, carrying out rapid matching. In the invention, multi-characteristic fusion and a Hash learning method are adopted, and the characteristic point is expressed in a binary system Hash code form; problems that calculating is complex and a matching speed is slow by using a traditional floating point type characteristic descriptor are overcome, and a characteristic matching method is simplified; and compared to a characteristic descriptor of a single characteristic, by using the method of the invention, high distinguishing performance is possessed, the matching speed is fast and accuracy is high.

Description

technical field [0001] The invention relates to a fast matching algorithm for aerial images, in particular to a fast matching algorithm for aerial images based on multi-feature hash learning, which belongs to the technical field of digital image processing. Background technique [0002] Aerial image matching technology is a research hotspot in the fields of computer vision, image processing and computer graphics. It uses multiple images with overlapping areas to generate high-resolution panoramic images. It has great potential in the fields of scene reconstruction, disaster prevention, environmental monitoring, and remote sensing images. Wide application value. Due to the large amount of data and high resolution of aerial images, how to construct efficient feature descriptors is the key to achieve rapid matching of aerial images. [0003] The local feature matching of aerial images is mainly divided into two steps: feature point detection and feature descriptor construction...

AI Technical Summary

Problems solved by technology

[0005] The main purpose of the present invention is to overcome the deficiencies in the prior art, to provide a fast matching algorithm for aerial images based on multi-feature hash learning, which expresses feature points in the form of binary hash codes, and solves problems based on traditional floating-point feature descriptions. Solve the problem of low matching efficiency in sub-time, and greatly improve the discrimination and matching accuracy of feature descriptors

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