A fast and mobile system for registration of low-altitude visual and thermal aerial images using multiple small-scale UAVs

Yahyanejad, Saeed; Rinner, Bernhard

doi:10.1016/j.isprsjprs.2014.07.015

Cited by 80 publications

(52 citation statements)

References 31 publications

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“…In most applications, however, thermal cameras are not equipped with an integrated RGB camera, or the simultaneous triggering of the RGB and thermal camera is difficult or practically not feasible. Yahyanejad and Rinner [36] developed an algorithm to co-register RGB and thermal images. However, their algorithm was so far only tested on datasets with high contrasts in both the RGB and the thermal images, and moreover, their algorithm is not (yet) publicly available.…”

Section: )mentioning

confidence: 99%

Optimizing the Processing of UAV-Based Thermal Imagery

2017

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Abstract:The current standard procedure for aligning thermal imagery with structure-from-motion (SfM) software uses GPS logger data for the initial image location. As input data, all thermal images of the flight are rescaled to cover the same dynamic scale range, but they are not corrected for changes in meteorological conditions during the flight. This standard procedure can give poor results, particularly in datasets with very low contrast between and within images or when mapping very complex 3D structures. To overcome this, three alignment procedures were introduced and tested: camera pre-calibration, correction of thermal imagery for small changes in air temperature, and improved estimation of the initial image position by making use of the alignment of RGB (visual) images. These improvements were tested and evaluated in an agricultural (low temperature contrast data) and an afforestation (complex 3D structure) dataset. In both datasets, the standard alignment procedure failed to align the images properly, either by resulting in point clouds with several gaps (images that were not aligned) or with unrealistic 3D structure. Using initial thermal camera positions derived from RGB image alignment significantly improved thermal image alignment in all datasets. Air temperature correction had a small yet positive impact on image alignment in the low-contrast agricultural dataset, but a minor effect in the afforestation area. The effect of camera calibration on the alignment was limited in both datasets. Still, in both datasets, the combination of all three procedures significantly improved the alignment, in terms of number of aligned images and of alignment quality.

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Section: )mentioning

confidence: 99%

Optimizing the Processing of UAV-Based Thermal Imagery

2017

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“…Thus, the inherent geometric deformations of UAVs images inevitably lead to a burgeoning interest in exploring the geographical registration techniques of UAVs image preprocessing (Chang-Chun et al 2011;Kekec, Yildirim, and Unel 2014;Xiang and Tian 2011;Yahyanejad and Rinner 2015). However, multispectral information dominates the application of UAVs images in vegetation monitoring, such as inter-class vegetation classification (Pérez-Ortiz et al 2016;Tian et al 2013), intra-class vegetation classification (Feng et al 2015), vegetation coverage (Torres-Sánchez, López-Granados, and Peña 2015), corn lodging (Li et al 2014), and the like.…”

Section: Introductionmentioning

confidence: 99%

A physical-based atmospheric correction algorithm of unmanned aerial vehicles images and its utility analysis

Liu

et al. 2016

International Journal of Remote Sensing

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Unmanned aerial vehicles (UAVs)-based environmental studies are gaining space in recent years due to their advantages of minimal cost, flexibility, and very high spatial resolution. Researchers can acquire imagery according to their schedule and convenience with the option of alternating the sensors working in visible, infrared, and microwave wavelengths. The recent developments in UAVs and in the associated image-processing techniques extend the fields of UAVs application. Inherent geometric deformation of UAVs images inevitably leads to burgeoning interest in exploring the geographical registration techniques of UAVs images preprocessing. However, atmospheric correction had been generally neglected due to the low altitudes of UAVs platforms. The path radiance of low-latitude atmosphere misleads the reflectance of target objects. Thus, a valid atmospheric correction is essential in the cases where vegetation indices (VIs) are adopted in vegetation monitoring. The off-the-shelf atmospheric correction algorithms adopted in satellite-based remote sensing are typically ill-suited for UAVs-based images due to the distinctly different altitudes and radiation transfer modes. This article identified the effect of atmospheric attenuation for spectral data collected by UAVs sensors of different altitudes and developed a physical-based atmospheric correction algorithm of UAVs images. Field-measured reflectance spectrum was essential in modelling. A sunny and dry day and a flat terrain were the two prerequisites to ensure the general application of the developed algorithm. A case study was subsequently carried out to verify the utility of the developed algorithm, and the results showed that VIs based on the UAVs images of different altitudes had a similar ability in vegetation assessment as groundbased recordings. However, the assessment accuracy could be clearly improved by using the developed atmospheric correction algorithm. ARTICLE HISTORY

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“…In recent years, the technology of multirotor models has developed considerably, resulting in stable flight and relatively low maintenance cost [6]. Two families of unmanned aircraft were considered in this research.…”

Section: E3s Web Of Conferencesmentioning

confidence: 99%

Unmanned Aerial Vehicles for Environmental Monitoring with Special Reference to Heat Loss

2017

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Abstract. This paper presents the design and implementation of device for remote and automatic monitoring of temperature field of large objects. The project aimed to create a quadcopter flying platform equipped with a thermal imaging camera. The object of the research was district heating installations above ground and underground. The results of the work on the implementation of low-cost (below 750 EUR) and efficient heat loss monitoring system. The system consists of a small (<2kg) multirotor platform. To perform thermal images micro camera FlirOne with microcomputer Raspberry Pi3 was used. Exploitation of UAVs in temperature field monitoring reveals only a fraction of their capabilities. The fast-growing multirotor platform market continues to deliver new solutions and improvements. Their use in monitoring the environment is limited only by the imagination of the user.

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A fast and mobile system for registration of low-altitude visual and thermal aerial images using multiple small-scale UAVs

Cited by 80 publications

References 31 publications

Optimizing the Processing of UAV-Based Thermal Imagery

Optimizing the Processing of UAV-Based Thermal Imagery

A physical-based atmospheric correction algorithm of unmanned aerial vehicles images and its utility analysis

Unmanned Aerial Vehicles for Environmental Monitoring with Special Reference to Heat Loss

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