Photogrammetric analysis of multispectral and thermal close-range images

Yıl 2021, Cilt: 3 Sayı: 1, 29 - 36, 15.06.2021

Özgün Akçay

https://doi.org/10.53093/mephoj.919916

Cited By: 3

Öz

Sensors capable of multispectral and thermal imaging beyond visible bands offer many analysis possibilities for environmental monitoring. Different sensor images constitute an important source of information especially in the fields of agriculture, forestry, geology and energy. Photogrammetric studies have been affected by this development in recent years and have been used in the production of multispectral and thermal models besides the RGB model. However, due to geometric and radiometric resolution differences, it is difficult to combine or evaluate models produced from different types of sensors. In this study, the three-dimensional test field images obtained with RGB, multispectral and thermal sensors were oriented and modeled photogrammetrically. The accuracies of the control points on the produced models were compared and discussed. When the results are examined, control point accuracy was obtained as almost similar as in the RGB model after the orientation based on automatic feature matching. Automatic feature detection and matching in thermal images were not robustly produced due to low geometric resolution. For this reason, manual measurements were performed in thermal images, and the photogrammetric orientation and adjustment process was done accordingly. The fused evaluation approach considering RGB, multispectral and thermal images in one photogrammetric model was also implemented and discussed.

Anahtar Kelimeler

Photogrammetry, Remote sensing, Multispectral images, Thermal images, Close-range

Kaynakça

• Athanasopoulos N & Siakavellas N J (2017). Smart patterned surfaces with programmable thermal emissivity and their design through combinatorial strategies. Scientific reports, 7(1), 1-16.
• Avdelidis N P & Moropoulou A (2003). Emissivity considerations in building thermography. Energy and Buildings, 35(7), 663-667.
• Biass S Orr T R, Houghton B F, Patrick M R, James M R & Turner N (2019). Insights into pāhoehoe lava emplacement using visible and thermal structure‐from‐motion photogrammetry. Journal of Geophysical Research: Solid Earth, 124(6), 5678-5695.
• Candiago S, Remondino F, De Giglio M, Dubbini M & Gattelli M (2015). Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote sensing, 7(4), 4026-4047.
• de Lima R S, Lang M, Burnside N G, Peciña M V, Arumäe T, Laarmann D, ... & Sepp K (2021). An Evaluation of the Effects of UAS Flight Parameters on Digital Aerial Photogrammetry Processing and Dense-Cloud Production Quality in a Scots Pine Forest. Remote Sensing, 13(6), 1121.
• Edelman G J & Aalders M C (2018). Photogrammetry using visible, infrared, hyperspectral and thermal imaging of crime scenes. Forensic science international, 292, 181-189.
• Erenoglu R C, Akcay O & Erenoglu O (2017). An UAS-assisted multi-sensor approach for 3D modeling and reconstruction of cultural heritage site. Journal of cultural heritage, 26, 79-90.
• Fraser C S (2013). Automatic camera calibration in close range photogrammetry. Photogrammetric Engineering & Remote Sensing, 79(4), 381-388.
• Guo Y, Senthilnath J, Wu W, Zhang X, Zeng Z & Huang H (2019). Radiometric calibration for multispectral camera of different imaging conditions mounted on a UAV platform. Sustainability, 11(4), 978.
• Matese A & Di Gennaro S F (2018). Practical applications of a multisensor UAV platform based on multispectral, thermal and RGB high resolution images in precision viticulture. Agriculture, 8(7), 116.
• Mello Román J C, Vázquez Noguera J L, Legal-Ayala H, Pinto-Roa D P, Gomez-Guerrero S & García Torres M (2019). Entropy and contrast enhancement of infrared thermal images using the multiscale top-hat transform. Entropy, 21(3), 244.
• Minařík R & Langhammer J (2016). Use of a multıspectral uav photogrammetry for detectıon and trackıng of forest dısturbance dynamıcs. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 41.
• Murtiyoso A, Grussenmeyer P, Börlin N, Vandermeerschen J & Freville T (2018). Open source and independent methods for bundle adjustment assessment in close-range UAV photogrammetry. Drones, 2(1), 3.
• Nebiker S, Annen A, Scherrer M & Oesch D (2008). A light-weight multispectral sensor for micro UAV—Opportunities for very high resolution airborne remote sensing. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, 37(B1), 1193-1200.
• Raeva P L, Šedina J & Dlesk A (2019). Monitoring of crop fields using multispectral and thermal imagery from UAV. European Journal of Remote Sensing, 52(sup1), 192-201.
• Ribeiro-Gomes K, Hernández-López D, Ortega J F, Ballesteros R, Poblete T & Moreno M A (2017). Uncooled thermal camera calibration and optimization of the photogrammetry process for UAV applications in agriculture. Sensors, 17(10), 2173.
• Sankey J B, Sankey T T, Li J, Ravi S, Wang G, Caster J & Kasprak A (2021). Quantifying plant-soil-nutrient dynamics in rangelands: Fusion of UAV hyperspectral-LiDAR, UAV multispectral-photogrammetry, and ground-based LiDAR-digital photography in a shrub-encroached desert grassland. Remote Sensing of Environment, 253, 112223.
• Saura J R, Reyes-Menendez A & Palos-Sanchez P (2019). Mapping multispectral Digital Images using a Cloud Computing software: applications from UAV images. Heliyon, 5(2), e01277.
• Turner R M, MacLaughlin M M & Iverson S R (2020). Identifying and mapping potentially adverse discontinuities in underground excavations using thermal and multispectral UAV imagery. Engineering Geology, 266, 105470.
• Van der Sluijs J, Kokelj S V, Fraser R H, Tunnicliffe J & Lacelle D (2018). Permafrost terrain dynamics and infrastructure impacts revealed by UAV photogrammetry and thermal imaging. Remote Sensing, 10(11), 1734.
• Wakeford Z E, Chmielewska M, Hole M J, Howell J A & Jerram D A (2019). Combining thermal imaging with photogrammetry of an active volcano using UAV: an example from Stromboli, Italy. The Photogrammetric Record, 34(168), 445-466.
• Wang J, Wang L, Jia M, He Z & Bi L (2020). Construction and optimization method of the open-pit mine DEM based on the oblique photogrammetry generated DSM. Measurement, 152, 107322.
• Webster C, Westoby M, Rutter N & Jonas T (2018). Three-dimensional thermal characterization of forest canopies using UAV photogrammetry. Remote Sensing of Environment, 209, 835-847.
• Wewel F, Scholten F & Gwinner K (2000). High resolution stereo camera (HRSC)-multispectral 3D-data acquisition and photogrammetric data processing. Canadian Journal of Remote Sensing, 26(5), 466-474.
• Xu Z, Shen X, Cao L, Coops N C, Goodbody T R, Zhong T, ... & Wu X (2020). Tree species classification using UAS-based digital aerial photogrammetry point clouds and multispectral imageries in subtropical natural forests. International Journal of Applied Earth Observation and Geoinformation, 92, 102173.
• Zefri Y, ElKettani A, Sebari I & Ait Lamallam S (2018). Thermal infrared and visual inspection of photovoltaic installations by UAV photogrammetry—application case: morocco. Drones, 2(4), 41.
• Zumr D, David V, Jeřábek J, Noreika N & Krása J (2020). Monitoring of the soil moisture regime of an earth-filled dam by means of electrical resistance tomography, close range photogrammetry, and thermal imaging. Environmental Earth Sciences, 79(12), 1-11.