Generation of Multitemporal Thermal Orthophotos From Uav Data

Pech, K.; Stelling, N.; Karrasch, Pierre; Maas, Hans‐Gerd

doi:10.5194/isprsarchives-xl-1-w2-305-2013

Cited by 18 publications

(17 citation statements)

References 6 publications

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“…Unfortunately, processing of thermal data with SfM does not always work flawlessly, and several studies have reported that SfM was not able to align thermal datasets properly [14,25,34]. In these cases, thermal maps could not be created or were created manually, by mosaicking separate images which were georeferenced manually using GCPs.…”

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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“…As for the descriptor, Samantha implements a 128-dimensional radial descriptor (similar to the log-polar grid of GLOH described by (Mikolajczyk and Schmid, 2005)), based on the accumulated response of steerable derivative filters. This combination of detector/descriptor performs in a comparable way to SIFT (Lowe, 2004), which is proved to be well suited for features detection also in low resolution thermal imagery (Pech et al, 2013), and at the same time avoids patent issues. Only a given number of key-points with the overall strongest response are retained.…”

Section: Structure-and-motionmentioning

confidence: 99%

“…The geometric calibration of a thermographic camera has been studied in some works (Luhmann et al, 2013, Lagüela et al, 2011. (Hoegner and Stilla, 2015) propose a method for automatic texturing of building * Corresponding author facades from terrestrial thermal infrared image sequences, while (Pech et al, 2013) analyze the problem of using a TIR camera on an UAV for acquiring multi-temporal thermal images and generating thermal orthophotos. In (Khodaei et al, 2015) it is demonstrated that Digital Surface Models (DSM) generated from aerial thermal imagery can have comparable accuracy with respect to the one derived from visible images, while in (Hoegner et al, 2014) a fusion of time-of-flight depth images and TIR images is used to create an accurate 3D point cloud for subsequent scene segmentation and people detection.…”

Section: Introductionmentioning

confidence: 99%

Photogrammetric 3d Building Reconstruction From Thermal Images

Maset

Fusiello

Crosilla

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper addresses the problem of 3D building reconstruction from thermal infrared (TIR) images. We show that a commercial Computer Vision software can be used to automatically orient sequences of TIR images taken from an Unmanned Aerial Vehicle (UAV) and to generate 3D point clouds, without requiring any GNSS/INS data about position and attitude of the images nor camera calibration parameters. Moreover, we propose a procedure based on Iterative Closest Point (ICP) algorithm to create a model that combines high resolution and geometric accuracy of RGB images with the thermal information deriving from TIR images. The process can be carried out entirely by the aforesaid software in a simple and efficient way.

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“…For most modern RGB cameras, a typical UAV flight campaign acquires enough sharp and properly illuminated images and therefore the unknown camera calibration coefficients are estimated during the image alignment step, always under the assumption enough tie points are found. The workflow for the interior calibration of thermal cameras is well documented in the literature (Luhmann et al, 2010, Hartmann et al, 2012, Pech et al, 2013. Although most authors recommend to use an aluminum plate with coded targets or similar structures to get significant thermal reflection differences, it turned out that also printed paper is sufficient.…”

Section: Geometric Camera Calibrationmentioning

confidence: 99%

Thermal Remote Sensing With Uav-Based Workflows

Boesch

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Climate change will have a significant influence on vegetation health and growth. Predictions of higher mean summer temperatures and prolonged summer draughts may pose a threat to agriculture areas and forest canopies. Rising canopy temperatures can be an indicator of plant stress because of the closure of stomata and a decrease in the transpiration rate. Thermal cameras are available for decades, but still often used for single image analysis, only in oblique view manner or with visual evaluations of video sequences. Therefore remote sensing using a thermal camera can be an important data source to understand transpiration processes. Photogrammetric workflows allow to process thermal images similar to RGB data. But low spatial resolution of thermal cameras, significant optical distortion and typically low contrast require an adapted workflow. Temperature distribution in forest canopies is typically completely unknown and less distinct than for urban or industrial areas, where metal constructions and surfaces yield high contrast and sharp edge information. The aim of this paper is to investigate the influence of interior camera orientation, tie point matching and ground control points on the resulting accuracy of bundle adjustment and dense cloud generation with a typically used photogrammetric workflow for UAVbased thermal imagery in natural environments.

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Generation of Multitemporal Thermal Orthophotos From Uav Data

Cited by 18 publications

References 6 publications

Optimizing the Processing of UAV-Based Thermal Imagery

Optimizing the Processing of UAV-Based Thermal Imagery

Photogrammetric 3d Building Reconstruction From Thermal Images

Thermal Remote Sensing With Uav-Based Workflows

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