Hierarchical Motion Consistency Constraint for Efficient Geometrical Verification in UAV Image Matching

TL;DR

This paper introduces a hierarchical motion consistency constraint (HMCC) method for fast and efficient geometrical verification in UAV image matching, significantly reducing outliers and speeding up processing while maintaining accuracy.

Contribution

The paper presents a novel HMCC approach that simplifies spatial relationships and employs a voting scheme to efficiently eliminate outliers before RANSAC, improving UAV image matching performance.

Findings

HMCC effectively separates inliers from outliers in UAV images.

The HMCC-RANSAC algorithm achieves up to 6x speedup in outlier elimination.

The method maintains competitive orientation accuracy despite slight point loss.

Abstract

This paper proposes a strategy for efficient geometrical verification in unmanned aerial vehicle (UAV) image matching. First, considering the complex transformation model between correspondence set in the image-space, feature points of initial candidate matches are projected onto an elevation plane in the object-space, with assistant of UAV flight control data and camera mounting angles. Spatial relationships are simplified as a 2D-translation in which a motion establishes the relation of two correspondence points. Second, a hierarchical motion consistency constraint, termed HMCC, is designed to eliminate outliers from initial candidate matches, which includes three major steps, namely the global direction consistency constraint, the local direction-change consistency constraint and the global length consistency constraint. To cope with scenarios with high outlier ratios, the HMCC is achieved by using a voting scheme. Finally, an efficient geometrical verification strategy is proposed by using the HMCC as a pre-processing step to increase inlier ratios before the consequent application of the basic RANSAC algorithm. The performance of the proposed strategy is verified through comprehensive comparison and analysis by using real UAV datasets captured with different photogrammetric systems. Experimental results demonstrate that the generated motions have noticeable separation ability, and the HMCC-RANSAC algorithm can efficiently eliminate outliers based on the motion consistency constraint, with a speedup ratio reaching to 6 for oblique UAV images. Even though the completeness sacrifice of approximately 7 percent of points is observed from image orientation tests, competitive orientation accuracy is achieved from all used datasets. For geometrical verification of both nadir and oblique UAV images, the proposed method can be a more efficient solution.