Robust remote sensing image feature point pair extraction method for nonlinear radiation distortion

Abstract

The invention discloses a robust remote sensing image feature point pair extraction method for nonlinear radiation distortion. The method comprises the following specific steps: carrying out image preprocessing on two acquired multi-modal remote sensing images; respectively extracting accurate angular points and edge feature points of the master image and the slave image, and obtaining feature points of each image; generating feature vectors of the feature points; registering the feature vectors of the feature points of the multi-modal image; further screening all candidate matching point pairs by using an RANSAC algorithm; and finally, obtaining accurate and uniform feature matching point pairs which are robust to nonlinear radiation distortion. A phase consistency measurement moment diagram robust to nonlinear radiation distortion is used as an object for feature point extraction, a large amount of accurate and robust feature point information can be obtained, and a mode of extracting Harris corner points by adopting image blocks is adopted, so that excessive concentration of image feature point distribution caused by non-uniform illumination radiation is effectively avoided, and the accuracy of image feature point extraction is improved. And the feature extraction efficiency is low.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, in particular to a robust remote sensing image feature point pair extraction method facing nonlinear radiation distortion. Background technique [0002] At present, multimodal remote sensing image feature matching has become a very important research direction in the field of remote sensing image processing. The matching information extracted from multimodal remote sensing image features can be widely used in many fields of national production and life and economic construction, such as natural disasters and environmental monitoring, multi-source information joint positioning, urban planning and major project construction, etc. However, the sources of multi-modal remote sensing images are different, and their resolution, spectrum, angle, and time details are different, which also brings great challenges to overcome the differences in heterogeneous image matching and improv...

AI Technical Summary

Problems solved by technology

[0006] Aiming at the problems that it is difficult to extract accurate and uniform robust feature information from existing multi-modal remote sensing images, resulting in difficulty in fusion of heterogene

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