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

Wenzhuo Li,Haigang Sui,Deren Li,Kaimin Sun,Ting Bai

doi:10.3390/rs9060625

Abstract

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.

Highlights

Detecting building change is of great importance for the dynamic monitoring of rapidly changing urban fringe areas and urban development analysis in general
We propose a novel bundle adjustments (BA) strategy called united bundle adjustment (UBA) for multi-temporal Unmanned aerial vehicles (UAVs)
We propose a novel method named UBA for multi-temporal UAV image co-registration for change detection (CD)

Summary

Introduction

Detecting building change is of great importance for the dynamic monitoring of rapidly changing urban fringe areas and urban development analysis in general. Unmanned aerial vehicles (UAVs) could provide a flexible and cost-effective solution for dynamic time-series change monitoring if change detection was less costly and time consuming. Successful change detection (CD) relies on highly accurate measurement of the relative position of multi-temporal spatial data. Highly accurate absolute position acquisition is not necessary. UAV images collected with consumer-level digital cameras have ultra-high spatial resolution in only the RGB bands. Single UAV image coverage is small, so more images must be collected to cover an area of interest. Conventional co-registration methods applied to multi-temporal UAV images obtained from these systems are inadequate, as they produce large numbers of images at varying photographical angles

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Discussion

Conclusion