The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XL-1/W2
16 Aug 2013
 | 16 Aug 2013


T. Sieberth, R. Wackrow, and J. H. Chandler

Keywords: Automation, Blur, Correction, Development, Digital, Image processing, Photogrammetry, UAV

Abstract. Unmanned aerial vehicles (UAV) have become an interesting and active research topic for photogrammetry. Current research is based on images acquired by an UAV, which have a high ground resolution and good spectral and radiometrical resolution, due to the low flight altitudes combined with a high resolution camera. UAV image flights are also cost effective and have become attractive for many applications including change detection in small scale areas.

One of the main problems preventing full automation of data processing of UAV imagery is the degradation effect of blur caused by camera movement during image acquisition. This can be caused by the normal flight movement of the UAV as well as strong winds, turbulence or sudden operator inputs. This blur disturbs the visual analysis and interpretation of the data, causes errors and can degrade the accuracy in automatic photogrammetric processing algorithms. The detection and removal of these images is currently achieved manually, which is both time consuming and prone to error, particularly for large image-sets. To increase the quality of data processing an automated filtering process is necessary, which must be both reliable and quick.

This paper describes the development of an automatic filtering process, which is based upon the quantification of blur in an image. A “shaking table” was used to create images with known blur during a series of laboratory tests. This platform can be moved in one direction by a mathematical function controlled by a defined frequency and amplitude. The shaking table was used to displace a Nikon D80 digital SLR camera with a user defined frequency and amplitude. The actual camera displacement was measured accurately and exposures were synchronized, which provided the opportunity to acquire images with a known blur effect. Acquired images were processed digitally to determine a quantifiable measure of image blur, which has been created by the actual shaking table function. Once determined for a sequence of images, a user defined threshold can be used to differentiate between “blurred” and "acceptable" images.

A subsequent step is to establish the effect that blurred images have upon the accuracy of subsequent measurements. Both of these aspects will be discussed in this paper and future work identified.