JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Effectiveness of Using the TIR Band in Landsat 8 Image Classification
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Effectiveness of Using the TIR Band in Landsat 8 Image Classification
Lee, Mi Hee; Lee, Soo Bong; Kim, Yongmin; Sa, Jiwon; Eo, Yang Dam;
  PDF(new window)
 Abstract
This paper discusses the effectiveness of using Landsat 8 TIR (Thermal Infrared) band images to improve the accuracy of landuse/landcover classification of urban areas. According to classification results for the study area using diverse band combinations, the classification accuracy using an image fusion process in which the TIR band is added to the visible and near infrared band was improved by 4.0%, compared to that using a band combination that does not consider the TIR band. For urban area landuse/landcover classification in particular, the producer’s accuracy and user’s accuracy values were improved by 10.2% and 3.8%, respectively. When MLC (Maximum Likelihood Classification), which is commonly applied to remote sensing images, was used, the TIR band images helped obtain a higher discriminant analysis in landuse/landcover classification.
 Keywords
Landsat 8;TIR Image;Band Combination;Visible and Near Infrared Image;Maximum Likelihood Classification;
 Language
English
 Cited by
 References
1.
Chander, G. and Markham, B. (2003), Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges, IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, No. 11, pp. 2674-2677. crossref(new window)

2.
Choi, J.W. (2011), Hybrid Pansharpening Algorithm of High-Spatial Resolution Satellite Images by Extracting Automatic Parameter Based on Spatial Correlation, Ph.D. dissertation, Graduate School of Seoul National University, Seoul, Korea, 114p. (in Korean with English abstract)

3.
Choi, J.W. and Kim, Y.I. (2010), Pan-sharpening algorithm of high-spatial resolution satellite image by using spectral and spatial characteristics, Journal of the Korean Society for Geospatial Information System, Vol. 2, No. 18, pp. 79-86. (in Korean with English abstract)

4.
Coll, C., Galve, J.M., Sanchez, J.M., and Caselles, V. (2010), Validation of Landsat-7/ETM+ TIR-band calibration and atmospheric correction with ground-based measurements, IEEE Transactions on Geoscience and Remote Sensing, Vol. 1, No. 48, pp. 547-555.

5.
Eo, Y.D. (1999), Development of the Training Normalization Algorithm and the Class Separability Measurement for Satellite Image Classification, Ph.D. dissertation, Graduate School of Seoul National University, Seoul, Korea, 113p. (in Korean with English abstract)

6.
Erdenechimeg, M., Choi, B.G., Na, Y.W., and Kim, T.H. (2010), Detection of land cover change using Landsat image data in desert area, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 2, pp. 471-476.

7.
FLAASH (2009), Atmospheric Correction Module: QUAC and FLAASH User’s Guide, Version 4. 7, ITT Visual Information Solutions Inc., Boulder, Co.

8.
Jeganathan, C., Hamm, N.A.S., Mukherjee, S., Atkinson, P.M., Raju, P.L.N., and Dadhwal, V.K. (2011), Evaluating a TIR image sharpening model over a mixed agricultural landscape in India, International Journal of Applied Earth Observation and Geoinformation, Vol. 13, No. 2, pp. 178-191. crossref(new window)

9.
Jensen, J.R. (2007), Remote Sensing of the Environment: An Earth Resource Perspective 2nd Edition, Prentice Hall Series in Geographic Information Science, Pearson Prentice Hal Inc, New Jersey.

10.
Jung, H.S. and Park, S.W. (2014), Multi-sensor fusion of Landsat 8 TIR and panchromatic images, Sensors, Vol. 14, No. 2, pp. 24425-24440. crossref(new window)

11.
Kim, Y.H. (2008), Modified Substitute Wavelet Image Fusion Method-Application to IKONOS Image-, Master’s thesis, Graduate School of Seoul National University, Seoul, Korea, 46p. (in Korean with English abstract)

12.
Kwon, B.K., Yamada, K., Niren, T., and Jo, M.H. (2003), A study on the landcover classification using band ratioing data of Landsat TM, Journal of the Korean Association of Geographic Information Studies, Vol. 2, No. 6, pp. 80-91. (in Korean with English abstract)

13.
Lee, H.J., Kim, S.W., Brioude, J., Cooper, O.R., Frost, G.J., Kim, C.H., Park, R.J., Trainer, M., and Woo, J.H. (2014), Transport of NOx in East Asia identified by satellite and in situ measurements and lagrangian particle dispersion model simulations, Journal of Geophysical Research: Atmospheres, Vol. 119, No. 5, pp. 2574-2596. crossref(new window)

14.
Lee, S.B., La, P.H., Eo, Y.D., and Pyeon, M.W. (2015), Generation of simulated image from atmospheric corrected Landsat TM images, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 33, No. 1, pp. 1-9. (in Korean with English abstract) crossref(new window)

15.
Lee, H.S., Oh, K.Y., and Jung, H.S. (2014), Comparative analysis of image fusion methods according to spectral responses of high-resolution optical sensors, Korean Journal of Remote Sensing, Vol. 30, No. 2, pp. 227-239. (in Korean with English abstract) crossref(new window)

16.
Moon, C.Y., Lee, J.C., and Koo, J.H. (2012), U-city Policy Handbook: A Guide to the Ubiquitous City Policy of Korea, Sigma Press, Korea.

17.
Park, M.E., Song, C.H., Park, R.S., Lee, J., Kim, J., Lee, S., Woo, J.-H., Carmichael, G.R., Eck, T.F., Holben, B.N., Lee, S.-S., Song, C.K., and Hong, Y.D. (2014), New approach to monitor transboundary particulate pollution over Northeast Asia, Atmospheric Chemistry and Physics, Vol. 14, No. 2, pp. 659-674. crossref(new window)

18.
Schneider, K. and Mauser, W. (1996), Processing and accuracy of Landsat thematic mapper data for lake surface temperature measurement, International Journal of Remote Sensing, Vol. 17, No. 11, pp. 2027-2041. crossref(new window)

19.
Sugumaran, R., Pavuluri, M.K., and Zerr, D. (2003), The use of high-resolution imagery for identification of urban climax forest species using traditional and rule-based classification approach, IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, No. 9, pp. 1933-1939. crossref(new window)

20.
Warner, T.A. and Nerry, F. (2009), Does single broadband or multispectral TIR data add information for classification of visible, near-and shortwave infrared imagery of urban areas?, International Journal of Remote Sensing, Vol. 30, No. 9, pp. 2155-2171. crossref(new window)

21.
Zhang, J., Wang, Y., and Li, Y. (2006), A C++ program for retrieving land surface temperature from the data of Landsat TM/ETM+ band6, Computers & Geosciences, Vol. 32, No. 10, pp. 1796-1805. crossref(new window)