Browse > Article
http://dx.doi.org/10.7848/ksgpc.2018.36.6.555

Accuracy Assessment of Sharpening Algorithms of Thermal Infrared Image Based on UAV  

Park, Sang Wook (Dept. of Civil Engineering, Chungbuk National University)
Choi, Seok Keun (Dept. of Civil Engineering, Chungbuk National University)
Choi, Jae Wan (Dept. of Civil Engineering, Chungbuk National University)
Lee, Seung Ki (Terrapix)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.36, no.6, 2018 , pp. 555-563 More about this Journal
Abstract
Thermal infrared images have the characteristic of being able to detect objects that can not be seen with the naked eye and have the advantage of easily obtaining information of inaccessible areas. However, TIR (Thermal InfraRed) images have a relatively low spatial resolution. In this study, the applicability of the pansharpening algorithm used for satellite imagery on images acquired by the UAV (Unmanned Aerial Vehicle) was tested. RGB image have higher spatial resolution than TIR images. In this study, pansharpening algorithm was applied to TIR image to create the images which have similar spatial resolution as RGB images and have temperature information in it. Experimental results show that the pansharpening algorithm using the PC1 band and the average of RGB band shows better results for the quantitative evaluation than the other bands, and it has been confirmed that pansharpening results by ATWT (${\grave{A}}$ Trous Wavelet Transform) exhibit superior spectral resolution and spatial resolution than those by HPF (High-Pass Filter) and SFIM (Smoothing Filter-based Intensity Modulation) pansharpening algorithm.
Keywords
Pansharpening Algorithm; Thermal Infrared Images; Unmanned Aerial Vehicle; Spatial Resolution;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Choi, J.W. (2015), Comparison of image sharpening algorithm for fusion of the airborne hyperspectral and RGB imagery, Journal of the Institute of Construction Technology, Vol. 34, No. 1, pp. 73-80. (in Korean with English abstract)
2 Dalla Mura, M., Vivone, G., Restaino, R., Addesso, P., and Chanussot, J. (2015). Global and local Gram-Schmidt methods for hyperspectral pansharpening, International Geoscience and Remote Sensing Symposium (IGARSS), IEEE, 26-31 July, Milan, Italy, pp. 37-40.
3 Gonzalez Audicana, M., Otazu, X., Fors, O., and Seco, A. (2005), Comparison between Mallat's and the a trous discrete wavelet transform based algorithms for the fusion of mulitispectral and panchromatic images, International Journal of Remote Sensing, Vol. 26, No. 3, pp. 595-614.   DOI
4 Grand, O.M., Ochagavia, H., Baluja, J., Diago, M.P., and Tardaguila, J. (2016), Thermal imaging to detect spatial and temporal variation in the water status of grapevine, The Journal of Horticultural Science and Bio technology, Vol. 91, No. 1, pp. 43-54.   DOI
5 Hanif, M. and Ali, U. (2006), Optimized visual and thermal image fusion for efficient face recognition, International Conference on Information Fusion-2006, IEEE, 10-13 July, Florence, Italy, pp. 1-6.
6 Ishimwe, R., Abutalen, K., and Ahmed, F. (2014), Applications of thermal imaging in agriculture-A review, Advances in Remote Sensing, Vol. 3, No. 3, pp. 128-140.   DOI
7 Jung, H.S, and Park, S.W. (2014) Multi-sensor fusion of Landsat 8 thermal infrared(TIR) and panchromatic(PAN) images, Sensors, Vol. 14, No. 12, pp. 24425-24440.   DOI
8 Kim, G.H. (2017), Generation of Land Cover Map Using Orthophoto and DSM Based on Fixed-wing Drone, Master's thesis, Chungbuk National University, Cheongju, Korea, 65p. (in Korean with English abstract)
9 Kim, Y.H., Choi, J.W., Kim, H.J., and Kim, Y.I. (2009), Modified a'trous algorithm based wavelet pan-sharpening method using IKONOS image, Journal of the Korean Society of Civil Engineers, Vol. 29, No. 2D, pp. 305-309. (in Korean with English abstract)
10 Ku C.Y. (2002), The study of image fusion for the analysis of satellite imagery data, The Journal of GIS Association of Korea, Vol. 10, No. 2, pp. 345-363. (in Korean with English abstract)
11 Park, S.W. (2018), Comparison Among Sharpening Algorithms of Thermal Image Based on UAV, Master's thesis, Chungbuk National University, Cheongju, Korea, 43p.
12 Wang, Z. and Bovik, A.C. (2002), A universal image quality index, IEEE Signal Processing Letters, Vol. 9, No. 3, pp. 81-84.   DOI
13 Ranchin, T. and Wald, L. (2000), Fusion of high spatial and spectral resolution images: the ARSIS concept and its implementation, Photogrammetric Engineering and Remote Sensing, Vol. 66, No. 1, pp. 49-61.
14 Ryu, T.H. and Um, J.S. (2013), Evaluating changing trends of surface temperature in winter according to rooftop color using remotely sensed thermal infrared, The Journal of GIS Association of Korea, Vol. 21, No. 1, pp. 345-363. (in Korean with English abstract)
15 Vivone, G., Alparone, L., Chanussot, J., Dalla Mura, M., Garzelli, A., Licciardi, G.A., and Wald, L. (2015), A critical comparison among pansharpening algorithms, IEEE Transactions on Geoscience and Remote Sensing, Vol. 53, No. 5, pp. 2565-2586.   DOI
16 Weinmann, M., Leitloff, J., Hoegner, L., Jutzi, B., Stilla, U., and Hinz, S. (2014), Thermal 3D mapping for object detection in dynamic scenes, ISPRS, Technical Commission Symposium, 17-20 November, Denver, USA, Vol. 2, No. 1, pp. 53-60.
17 Zhou. J., Civco, D.L., and Silander, J.A.(1998), A wavelet transform method to merge Landsat TM and SPOT panchromatic data, International Journal of Remote Sensing, Vol. 19, No. 4, pp. 747-757.
18 Yu, Y.C., Im, K.M., Seoung, N.H., Kim, D.H., Lee, K.H., and Kang, S.J. (2016), Study on utilization for reservoir diagnosis through thermal image UAV, Proceedings of KSEG 2016 Spring Conference, The Korean Society of Engineering Geology, 7-8 April, Jeju, Korea, pp. 203-204.
19 Zhang, Y. (2015), A new merging method and its spectral and spacial effects, International Journal of Remote Sensing, Vol. 20, No. 10, pp. 2003-2014.   DOI