Online Action Change Detection for Automatic Vision-based Ground Control of Aircraft

2022 ◽  
Author(s):  
Qingze Huo ◽  
Yifeng Shi ◽  
Chang Liu ◽  
Vahid Tarokh ◽  
Silvia Ferrari
Keyword(s):  
Change Detection ◽  
Ground Control ◽  
Action Change

Extracting photogrammetric ground control from lidar DEMs for change detection

2006 ◽  
Vol 21 (116) ◽  
pp. 312-328 ◽  
Author(s):  
Timothy D. James ◽  
Tavi Murray ◽  
Nicholas E. Barrand ◽  
Stuart L. Barr
Keyword(s):  
Change Detection ◽  
Ground Control

UAV-derived change detection without ground control points, an example from the cliff coast of Rügen

2020 ◽  
Author(s):  
Kristen Cook ◽  
Michael Dietze
Keyword(s):  
Change Detection ◽  
Subsurface Water ◽  
Ground Control ◽  
Control Points ◽  
Model Quality ◽  
Antecedent Precipitation ◽  
Ground Control Points ◽  
Comparative Accuracy ◽  
Coastal Cliffs ◽  
Aerial Vehicle

<p><span>High resolution topographic models generated from repeat unmanned aerial vehicle (UAV) surveys and structure from motion (SfM) are increasingly being used to investigate landscape changes and geomorphic processes. Traditionally, accurate UAV surveys require the use of independently measured ground control points or highly accurate camera position measurements. However, in addition to accuracy in an absolute sense (how well modeled topography matches real topography), model quality can be expressed as accuracy in a comparative sense (the degree to which two models match each other). We present a simple SfM workflow for calculating pairs or sets of models with a high comparative accuracy, without the need for ground control points or a d</span><span>G</span><span>NSS equipped </span><span>UAV. The method is based on the automated detection of common tie points in stable portions of the survey area and, c</span><span>ompared to a standard SfM approach without ground control, reduces the level of change detection in our surveys from several meters to 10-15 cm. </span><span>We apply this</span><span> approach in a multi-year monitoring campaign of an 8 km stretch of coastal cliffs on the island of Rügen, Germany. We are able to detect numerous mass wasting events as well as bands of more diffuse erosion in chalk sections of the cliff. Both the cliff collapses and the diffuse erosion appear to be strongly influenced by antecedent precipitation over seasonal timescales, with much greater activity during the winter of 2017-2018, following an above average wet summer, than during the subsequent two winters, which both followed relatively dry summers. This points to the influence of subsurface water storage in modulating cliff erosion on Rügen.</span></p>

scholarly journals Short Communication: A simple workflow for robust low-cost UAV-derived change detection without ground control points

2019 ◽  
Author(s):  
Kristen L. Cook ◽  
Michael Dietze
Keyword(s):  
Change Detection ◽  
Low Cost ◽  
Point Clouds ◽  
Standard Approach ◽  
Ground Control ◽  
Control Points ◽  
Ground Control Points ◽  
3D Point Clouds ◽  
Tectonically Active ◽  
Comparative Accuracy

Abstract. High quality 3D point clouds generated from repeat camera-equipped unmanned aerial vehicle (UAV) surveys are increasingly being used to investigate landscape changes and geomorphic processes. Point cloud quality can be expressed as accuracy in a comparative (i.e., from survey to survey) and absolute (between survey and an external reference system) sense. Here we present a simple workflow for calculating pairs or sets of point clouds with a high comparative accuracy, without the need for ground control points or a dGPS equipped UAV. We demonstrate the efficacy of the new approach using a consumer-grade UAV in two contrasting landscapes: the coastal cliffs on the Island of Rügen, Germany, and the tectonically active Daan River gorge in Taiwan. Compared to a standard approach using ground control points, our workflow results in a nearly identical distribution of measured changes. Compared to a standard approach without ground control, our workflow reduces the level of change detection from several meters to 10–15 cm. This approach enables robust change detection using UAVs in settings where ground control is not possible.

scholarly journals CHANGE DETECTION IN PHOTOGRAMMETRIC POINT CLOUDS FOR MONITORING OF ALPINE, GRAVITATIONAL MASS MOVEMENTS

2020 ◽  
Vol V-2-2020 ◽  
pp. 687-693
Author(s):  
A. Dinkel ◽  
L. Hoegner ◽  
A. Emmert ◽  
L. Raffl ◽  
U. Stilla
Keyword(s):  
Change Detection ◽  
Noise Level ◽  
Point Clouds ◽  
Bundle Adjustment ◽  
Gravitational Mass ◽  
Ground Control ◽  
Mass Movements ◽  
Control Points ◽  
The Third ◽  
Ground Control Points

Abstract. This contribution discusses the accuracy and the applicability of Photogrammetric point clouds based on dense image matching for the monitoring of gravitational mass movements caused by crevices. Four terrestrial image sequences for three different time epochs have been recorded and oriented using ground control point in a local reference frame. For the first epoch, two sequences are recorded, one in the morning and one in the afternoon to evaluate the noise level within the point clouds for a static geometry and changing light conditions. The second epoch is recorded a few months after the first epoch where also no significant change has occurred in between. The third epoch is recorded after one year with changes detected. As all point clouds are given in the same local coordinate frame and thus are co-registered via the ground control points, change detection is based on calculating the Multiscale-Model-to-Model-Cloud distances (M3C2) of the point clouds. Results show no movements for the first year, but identify significant movements comparing the third epoch taken in the second year. Besides the noise level estimation, the quality checks include the accuracy of the camera orientations based on ground control points, the covariances of the bundle adjustment, and a comparison the Geodetic measurements of additional control points and a laser scanning point cloud of a part of the crevice. Additionally, geological measurements of the movements have been performed using extensometers.

scholarly journals Short Communication: Optimizing UAV-SfM based topographic change detection with survey co-alignment

2020 ◽  
Author(s):  
Tjalling de Haas ◽  
Wiebe Nijland ◽  
Brian W. McArdell ◽  
Maurice W. M. L. Kalthof
Keyword(s):  
Change Detection ◽  
Unmanned Aerial Vehicles ◽  
Structure From Motion ◽  
Short Communication ◽  
Swiss Alps ◽  
Ground Control ◽  
Control Points ◽  
Classical Approach ◽  
Ground Control Points ◽  
Surface Models

Abstract. High-quality digital surface models (DSMs) generated from structure-from-motion (SfM) based on imagery captured from unmanned aerial vehicles (UAVs), are increasingly used for topographic change detection. Classically, DSMs were generated for each survey individually and then compared to quantify topographic change, but recently it was shown that co-aligning the images of multiple surveys may enhance the accuracy of topographic change detection. Here, we use nine surveys over the Illgraben debris-flow torrent in the Swiss Alps to compare the accuracy of three approaches for UAV-SfM topographic change detection: (1) the classical approach where each survey is processed individually using ground control points (GCPs), (2) co-alignment of all surveys without GCPs, and (3) co-alignment of all surveys with GCPs. We demonstrate that compared to the classical approach co-alignment enhances the accuracy of topographic change detection by a factor 4 with GCPs and a factor 3 without GCPs, leading to xy and z offsets

scholarly journals Case Report: Optimization of Topographic Change Detection With UAV Structure-From-Motion Photogrammetry Through Survey Co-Alignment

2021 ◽  
Vol 2 ◽  
Author(s):  
Tjalling de Haas ◽  
Wiebe Nijland ◽  
Brian W. McArdell ◽  
Maurice W. M. L. Kalthof
Keyword(s):  
Change Detection ◽  
Structure From Motion ◽  
Swiss Alps ◽  
Ground Control ◽  
Control Points ◽  
Classical Approach ◽  
Absolute Accuracy ◽  
Ground Control Points ◽  
Surface Models ◽  
A Minor

High-quality digital surface models (DSMs) generated from structure-from-motion (SfM) based on imagery captured from unmanned aerial vehicles (UAVs), are increasingly used for topographic change detection. Classically, DSMs were generated for each survey individually and then compared to quantify topographic change, but recently it was shown that co-aligning the images of multiple surveys may enhance the accuracy of topographic change detection. Here, we use nine surveys over the Illgraben debris-flow torrent in the Swiss Alps to compare the accuracy of three approaches for UAV-SfM topographic change detection: 1) the classical approach where each survey is processed individually using ground control points (GCPs), 2) co-alignment of all surveys without GCPs, and 3) co-alignment of all surveys with GCPs. We demonstrate that compared to the classical approach co-alignment with GCPs leads to a minor and marginally significant increase in absolute accuracy. Moreover, compared to the classical approach co-alignment enhances the relative accuracy of topographic change detection by a factor 4 with GCPs and a factor 3 without GCPs, leading to xy and z offsets <0.1 m for both co-alignment approaches. We further show that co-alignment leads to particularly large improvements in the accuracy of poorly aligned surveys that have severe offsets when processed individually, by forcing them onto the more accurate common geometry set by the other surveys. Based on these results we advocate that co-alignment, preferably with GCPs to ensure a high absolute accuracy, should become common-practice in high-accuracy UAV-SfM topographic change detection studies for projects with sufficient stable areas.

Sign in / Sign up

Export Citation Format

Share Document