Rapid image registration implementation method based on space sparsity and SIFT feature extraction

Abstract

The invention discloses a rapid image registration implementation method based on space sparsity and SIFT feature extraction and mainly aims at solving the problem of feature mismatching due to frequently extracted feature points unstable in smooth region and texture region in a classical SIFT feature extraction algorithm. The rapid image registration implementation method based on the space sparsity and the SIFT feature extraction comprises the steps of 1) extracting the sparse regions of a reference image and an image to be matched, respectively, 2) extracting SIFT feature points from the sparse regions of the reference image and the image to be matched, 3) performing rough matching on the set of SIFT feature points extracted from the reference image and the image to be matched, 4) filtering out mismatch in the rough matching result by use of a random consistency estimation algorithm, and 5) realizing registration on two SAR images by use of an affine transformation coefficient obtained through affine transformation by utilizing the final matched point pairs of the reference image and the image to be matched. The rapid image registration implementation method based on the space sparsity and the SIFT feature extraction is capable of improving the refrigeration efficiency under the premise of guaranteeing the accuracy of the classical SIFT feature refrigeration algorithm and can be applied to the refrigeration processing of the SAR images.

Description

technical field [0001] The invention belongs to the technical field of image processing and relates to image registration, in particular to a rapid image registration implementation method based on spatial sparsity and SIFT feature extraction, which can be used for early registration of SAR image change detection, fusion, splicing, etc. Work. Background technique [0002] The image formed by synthetic aperture radar (SAR) has the characteristics of all-weather, all-time, high resolution and strong penetrating ability. It is one of the most representative means of earth observation today. Before splicing, fusion, change detection and other operations on SAR images, it is necessary to spatially register images from the same region, at different viewpoints at the same time or at different times of the same sensor to eliminate the time, angle, and The difference in environment and sensor imaging mechanism causes problems such as translation, rotation, stretching and local defo...

AI Technical Summary

Problems solved by technology

However, since SIFT uses a 128-dimensional vector to describe the feature points, the calculation cost and time complexity are increased when there are many feature points, and a better distinction between features is required when looking for the best match.

When using the SIFT feature extraction algorithm to process SAR images, not only can more SIFT feature points be extracted in areas with sparse structures such as straight lines, broken lines, and intersections, but also many features can be extracted in smooth areas and texture areas where the features are not obvious. points, but there are many similar points around the feature points of these smooth areas and texture areas, so it takes a lot of time and the features of these points are not obvious, and it is easy to form a mismatch

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