Co-Registration of Multitemporal Uav Image Datasets for Monitoring Applications: A New Approach

Abstract

Commission ICWG I/VbKEY WORDS: Multi-temporal, UAV, image-based approach, image registration, photogrammetry ABSTRACT:In the last years we have witnessed a rapid development of UAVs (Unmanned Aerial Vehicles), especially for image collection. One of the advantages is the possibility to perform high resolution and repeated flights in a cheap way to detect changes over time. Thus, dynamic scenes can be monitored acquiring image blocks in different epochs in a flexible way. Anyway, most of UAVs are not able to provide accurate direct geo-referencing information, so image blocks from different epochs still need to be co-registered to efficiently detect changes. This task is mostly completed using GCPs (Ground Control Points), although this approach is time consuming as manual intervention is needed. This paper aims at investigating new techniques to automate the co-registration of image blocks without the use of GCPs, just relying on an image based co-registration (IBCR) approach. The image alignment is initially performed on a reference (anchor) epoch and the registration of the following (slave) epochs is performed including some (anchor) images from the reference epoch with fixed external orientation parameters. This allows constraining the Bundle Block Adjustment of the slave epoch to be consistent with the reference one. The study involved the use of 10 multi-temporal image block over a large building construction site, and spanning a time frame of 2 years. Different tests have been performed for the reference image choice with a manual approach and then evaluating the reached accuracy. The performed tests on the chosen test site have shown that the accuracy of the proposed methodology provides results comparable to the common GCPs registration approach.

UAV-based Multispectral & Thermal dataset for exploring the diurnal variability, radiometric & geometric accuracy for precision agriculture

“…Multiple studies refer to the capabilities of these sensors (Elarab et al 2015;Gago et al 2015;Haghighattalab et al 2016;Holman et al 2016;Potgieter et al 2017;Raeva, Šedina, and Dlesk 2018;Sankaran et al 2015;Shi et al 2016;Zhang and Kovacs 2012). A few studies refer to the limitations and best practices, to achieve high-quality data (Aicardi et al 2016;Assmann et al 2018;Kelly et al 2019;Maes, Huete, and Steppe 2017;Peña et al 2015;Torres-Sánchez et al 2013). However, in existing studies the emphasis is on the differences or complementary use of different sensors while the effect of the diurnal differences in the data is not fully explored.…”