Multi-Temporal Image Co-Registration Of Uav Blocks: A Comparison Of Different Approaches

Traditionally, data co-registration of survey epochs in photogrammetry relied on Ground Control Points (GCP) to keep the reference system unchanged. In the last years, Unmanned Aerial Systems (UAV) are increasingly used in photogrammetric environmental monitoring. The diffusion of affordable UAV platforms equipped with GNSS (Global Navigation Satellite System) centimetre-grade receivers might reduce, but not eliminate, the need for GCP. Conversely, if GNSS-assisted orientation cannot be used or if additional ground control and reliability checks are required, alternatives to repeated GCP survey have been proposed, taking advantage of Structure from Motion (SfM) photogrammetry. In particular, co-registering different epochs image blocks together, identifying corresponding features, has been demonstrated as a viable and efficient approach. In this paper four different strategies easily implementable in a generic commercial photogrammetric software are presented and compared considering three different test sites in Italy subject to different amounts of environmental changes. The influence of the amount and distribution of inter-epoch corresponding points on the accuracy of the reconstruction is investigated. The results show that some of the tested strategies obtains very good results and can be used (although not needed) also in RTK centimetre-grade UAV surveys, leveraging the additional information coming from previous epochs survey to actually increase the survey accuracy and reliability

MULTI-TEMPORAL IMAGE CO-REGISTRATION OF UAV BLOCKS: A COMPARISON OF DIFFERENT APPROACHES

Abstract. Traditionally, data co-registration of survey epochs in photogrammetry relied on Ground Control Points (GCP) to keep the reference system unchanged. In the last years, Unmanned Aerial Systems (UAV) are increasingly used in photogrammetric environmental monitoring. The diffusion of affordable UAV platforms equipped with GNSS (Global Navigation Satellite System) centimetre-grade receivers might reduce, but not eliminate, the need for GCP. Conversely, if GNSS-assisted orientation cannot be used or if additional ground control and reliability checks are required, alternatives to repeated GCP survey have been proposed, taking advantage of Structure from Motion (SfM) photogrammetry. In particular, co-registering different epochs image blocks together, identifying corresponding features, has been demonstrated as a viable and efficient approach. In this paper four different strategies easily implementable in a generic commercial photogrammetric software are presented and compared considering three different test sites in Italy subject to different amounts of environmental changes. The influence of the amount and distribution of inter-epoch corresponding points on the accuracy of the reconstruction is investigated. The results show that some of the tested strategies obtains very good results and can be used (although not needed) also in RTK centimetre-grade UAV surveys, leveraging the additional information coming from previous epochs survey to actually increase the survey accuracy and reliability

Monitoring solifluction movement in space and time: A semi-automated high-resolution approach

Solifluction is the slow downslope movement of soil mass due to freeze-thaw processes. It is widespread on hillslopes in Polar and Alpine regions and contributes substantially to sediment transport. As solifluction lobe movement is in the order of millimeters to centimeters per year, it is difficult to measure with high spatial and temporal resolution and accuracy. In this study we developed a semi-automated approach to monitor movement using unmanned aerial vehicles, image co-alignment, and COSI-Corr (Co-registration of Optically Sensed Images and Correlation) to track slope movement from orthophotos. The method was applied on yearly images acquired between 2017 and 2021 of three solifluction lobes with different degrees of vegetation cover along an elevational gradient in Turtmann Valley, Swiss Alps. We found movement patterns across all three lobes with highest movement rates at the solifluction lobes center and lowest rates at lobe fronts. Overall, at the highest elevations (2560 m) lobe movement rates were highest with up to 14.0 cm yr−1 and intermediate elevations (2417 m) had the lowest values up to 2.9 cm yr−1. The lobe at the lowest elevation (2170 m) showed intermediate movement rates with up to 4.9 cm yr−1 for single years. Our monitoring approach provides yearly, spatially extensive movement estimates across the complete spatial extent of a lobe for each 1 cm2 of its surface, strongly increasing measurement resolution in comparison to traditional solifluction monitoring approaches using point measurements. In comparison to previous close-range remote sensing approaches, the use of a co-alignment procedure for the acquired drone data enabled a time-saving field setup without Ground Control Points (GCPs). The resulting high co-registration accuracy enabled us to detect solifluction movement if it exceeds 5 mm with sparse vegetation cover. Dense vegetation cover limited feature-tracking but detected movement rates and patterns are in the same order of magnitude and matched previous measurements using classical total station measurements at the lowest, mostly vegetated lobe. This study demonstrates the use of drone-based Digital Elevation Models (DEMs) and orthophotos in a semi-automated method which reaches the high spatiotemporal resolution necessary to detect subtle movements of solifluction lobes at yearly intervals at the sub-centimeter scale. This provides new insights into solifluction movement and how much it contributes to sediment transport. Therefore, our semi-automated approach has a great potential to uncover the fundamental processes and better understand solifluction movement

Improving UAV-SfM time-series accuracy by co-alignment and contributions of ground control or RTK positioning

Unmanned Aerial Vehicle Structure from Motion (UAV-SfM) photogrammetry is increasingly applied to topographic change detection, which requires multitemporal Digital Surface Models (DSMs) with high relative accuracy. Of these tools, Ground Control Points (GCPs) and an image processing method called co-alignment have so far shown promising results for change detection studies. However, there is still insufficient research on the extent of improving 3D model accuracy by combining these tools. In our study we assess absolute and relative accuracy of 120 DSMs generated through 24 workflows of UAV-SfM photogrammetry. Surveys were acquired with two different UAVs with Real Time Kinematic (RTK) or generic Global Navigation Satellite System (GNSS) positioning, and processed with varying combinations of survey co-alignment and GCPs. We show that co-alignment reduces relative errors to below 2 cm regardless of positioning quality. A single RTK survey in a co-aligned project is sufficient to obtain high absolute xy accuracy, but GCPs for at least one survey are still required to reduce absolute z error. We demonstrate that co-aligning RTK surveys with generic GNSS surveys results in RTK class accuracy for all surveys, even when mixed sensor grades are used. Our findings enable high-accuracy change detection with lower accuracy archived images when combined with RTK surveys. For future UAV-SfM change detection studies, we recommend to apply co-alignment for all studies, and where possible to include GCPs and RTK image coordinates in one survey to optimize absolute accuracy. Collecting and digitizing GCPs in multiple surveys has shown little additional benefit when co-alignment is applied and therefore may be omitted to save time, especially in challenging field conditions

A New Approach to Performing Bundle Adjustment for Time Series UAV Images 3D Building Change Detection

Successful change detection in multi-temporal images relies on high spatial co-registration accuracy. However, co-registration accuracy alone cannot meet the needs of change detection when using several ground control points to separately geo-reference multi-temporal images from unmanned aerial vehicles (UAVs). This letter reports on a new approach to perform bundle adjustment—named united bundle adjustment (UBA)—to solve this co-registration problem for change detection in multi-temporal UAV images. In UBA, multi-temporal UAV images are matched with each other to construct a unified tie point net. One single bundle adjustment process is performed on the unified tie point net, placing every image into the same coordinate system and thus automatically accomplishing spatial co-registration. We then perform change detection using both orthophotos and three-dimensional height information derived from dense image matching techniques. Experimental results show that UBA co-registration accuracy is higher than the accuracy of commonly-used approaches for multi-temporal UAV images. Our proposed preprocessing method extends the capacities of consumer-level UAVs so they can eventually meet the growing need for automatic building change detection and dynamic monitoring using only RGB band images

VGC 2023 - Unveiling the dynamic Earth with digital methods: 5th Virtual Geoscience Conference: Book of Abstracts

Conference proceedings of the 5th Virtual Geoscience Conference, 21-22 September 2023, held in Dresden. The VGC is a multidisciplinary forum for researchers in geoscience, geomatics and related disciplines to share their latest developments and applications.:Short Courses 9 Workshops Stream 1 10 Workshop Stream 2 11 Workshop Stream 3 12 Session 1 – Point Cloud Processing: Workflows, Geometry & Semantics 14 Session 2 – Visualisation, communication & Teaching 27 Session 3 – Applying Machine Learning in Geosciences 36 Session 4 – Digital Outcrop Characterisation & Analysis 49 Session 5 – Airborne & Remote Mapping 58 Session 6 – Recent Developments in Geomorphic Process and Hazard Monitoring 69 Session 7 – Applications in Hydrology & Ecology 82 Poster Contributions 9