SAR image matching algorithm based on multi-class features

TL;DR

This paper introduces a SAR image matching algorithm utilizing multi-class features, specifically lines and regions, to improve robustness and accuracy in image matching, especially under varying perspectives and lighting conditions.

Contribution

The novel approach combines line and region features with prior knowledge and line detection techniques to enhance SAR image matching accuracy and robustness.

Findings

High-precision matching results achieved

Improved robustness to perspective and lighting changes

Reduced false positives in matching

Abstract

Synthetic aperture radar has the ability to work 24/7 and 24/7, and has high application value. Propose a new SAR image matching algorithm based on multi class features, mainly using two different types of features: straight lines and regions to enhance the robustness of the matching algorithm; On the basis of using prior knowledge of images, combined with LSD (Line Segment Detector) line detection and template matching algorithm, by analyzing the attribute correlation between line and surface features in SAR images, selecting line and region features in SAR images to match the images, the matching accuracy between SAR images and visible light images is improved, and the probability of matching errors is reduced. The experimental results have verified that this algorithm can obtain high-precision matching results, achieve precise target positioning, and has good robustness to changes in perspective and lighting. The results are accurate and false positives are controllable.