Patch-based Evaluation of Dense Image Matching Quality

TL;DR

This paper introduces a framework for evaluating the quality of dense image matching point clouds and DSMs, demonstrating their potential as cost-effective alternatives to laser scanning with detailed accuracy assessments.

Contribution

It presents a novel evaluation framework for dense image matching data, including error and noise analysis at local and block levels, and explores the impact of additional oblique images.

Findings

Optimal vertical accuracy is 0.1 GSD mean offset

Maximum offset reaches 1.0 GSD

Adding oblique images improves accuracy and noise levels

Abstract

Airborne laser scanning and photogrammetry are two main techniques to obtain 3D data representing the object surface. Due to the high cost of laser scanning, we want to explore the potential of using point clouds derived by dense image matching (DIM), as effective alternatives to laser scanning data. We present a framework to evaluate point clouds from dense image matching and derived Digital Surface Models (DSM) based on automatically extracted sample patches. Dense matching error and noise level are evaluated quantitatively at both the local level and whole block level. Experiments show that the optimal vertical accuracy achieved by dense matching is as follows: the mean offset to the reference data is 0.1 Ground Sampling Distance (GSD); the maximum offset goes up to 1.0 GSD. When additional oblique images are used in dense matching, the mean deviation, the variation of mean deviation and the level of random noise all get improved. We also detect a bias between the point cloud and DSM from a single photogrammetric workflow. This framework also allows to reveal inhomogeneity in the distribution of the dense matching errors due to over-fitted BBA network. Meanwhile, suggestions are given on the photogrammetric quality control.