Abstract

During the past years, UAVs (Unmanned Aerial Vehicles) became very popular as low-cost image acquisition platforms since they allow for high resolution and repetitive flights in a flexible way. One application is to monitor dynamic scenes. However, the fully automatic co-registration of the acquired multi-temporal data still remains an open issue. Most UAVs are not able to provide accurate direct image georeferencing and the co-registration process is mostly performed with the manual introduction of ground control points (GCPs), which is time consuming, costly and sometimes not possible at all. A new technique to automate the co-registration of multi-temporal high resolution image blocks without the use of GCPs is investigated in this paper. The image orientation is initially performed on a reference epoch and the registration of the following datasets is achieved including some anchor images from the reference data. The interior and exterior orientation parameters of the anchor images are then fixed in order to constrain the Bundle Block Adjustment of the slave epoch to be aligned with the reference one. The study involved the use of two different datasets acquired over a construction site and a post-earthquake damaged area. Different tests have been performed to assess the registration procedure using both a manual and an automatic approach for the selection of anchor images. The tests have shown that the procedure provides results comparable to the traditional GCP-based strategy and both the manual and automatic selection of the anchor images can provide reliable results.

Highlights

  • Multi-temporal data acquisition is required in many research and application fields

  • The automatic procedure shows better results compared to the RGCP solution for the vertical

  • This paper presents an automatic technique to register high resolution multi-temporal datasets paper presents an automatic techniquewithout to register multi-temporal fromThis

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Summary

Introduction

Images can provide colored georeferenced ortho-images, surface models and point clouds which allow a more comprehensive understanding of the scene For this reason, a lot of research in the last decade has been concentrated on using both terrestrial and aerial data to achieve reliable results with photogrammetric approaches, especially in the field of construction industry [8], disaster monitoring [9,10], urban development, documentation of archaeological sites [11,12] and agriculture and natural resources management [13]. All these works use images from single epochs as the Remote Sens. 2016, 8, 779; doi:10.3390/rs8090779 www.mdpi.com/journal/remotesensing

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