Browse > Article
http://dx.doi.org/10.7319/kogsis.2014.22.4.053

Unsupervised Classification of Landsat-8 OLI Satellite Imagery Based on Iterative Spectral Mixture Model  

Choi, Jae Wan (School of Civil Engineering, Chungbuk National University)
Noh, Sin Taek (School of Civil Engineering, Chungbuk National University)
Choi, Seok Keun (School of Civil Engineering, Chungbuk National University)
Publication Information
Journal of Korean Society for Geospatial Information Science / v.22, no.4, 2014 , pp. 53-61 More about this Journal
Abstract
Landsat OLI satellite imagery can be applied to various remote sensing applications, such as generation of land cover map, urban area analysis, extraction of vegetation index and change detection, because it includes various multispectral bands. In addition, land cover map is an important information to monitor and analyze land cover using GIS. In this paper, land cover map is generated by using Landsat OLI and existing land cover map. First, training dataset is obtained using correlation between existing land cover map and unsupervised classification result by K-means, automatically. And then, spectral signatures corresponding to each class are determined based on training data. Finally, abundance map and land cover map are generated by using iterative spectral mixture model. The experiment is accomplished by Landsat OLI of Cheongju area. It shows that result by our method can produce land cover map without manual training dataset, compared to existing land cover map and result by supervised classification result by SVM, quantitatively and visually.
Keywords
Spectral Mixture Analysis; Land Cover Map; Landsat OLI; Training Data;
Citations & Related Records
Times Cited By KSCI : 7  (Citation Analysis)
연도 인용수 순위
1 Baig M. H. A., Zhang, L., Shuai T. and Tong, Q., 2014, Derivation of a tasselled cap transformation based on landsat 8 at satellite reflectance, Remote Sensing Letters, Vol. 5, No. 5, pp. 423-431.   DOI
2 Bhatti, S. S. and Tripathi, B. K., 2014, Built-up area extraction using Landsat 8 OLI imagery, GIScience & Remote Sensing, Vol. 51, No. 4, pp. 445-467.   DOI
3 Chang, C. I. and Heinz, D. C., 2000, Constrained subpixel target detection for remotely sensed imagery, IEEE Transactions on Geoscience and Remote Sensing, Vol. 38, No. 3, pp. 1144-1159.   DOI   ScienceOn
4 Chi, J., 2013, Validation of the radiometric characteristics of landsat 8(LDCM) OLI sensor using band aggregation technique of EO-1 hyperion hyperspectral imagery, Korean Journal of Remote Sensing, Vol. 29, No. 4, pp. 399-406.   과학기술학회마을   DOI
5 Choi, S., Lee, S. and Wang, B., 2014, Analysis of vegetation cover fraction on landsat OLI using NDVI, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 32, No. 1, pp. 9-17.   과학기술학회마을   DOI
6 Ding, Y., Zhao, K., Zheng, X. and Jiang, T., 2014, Temporal dynamics of spatial heterogeneity over cropland quantified by time-series NDVI, near infrared and red reflectance of Landsat 8 OLI imagery, International Journal of Applied Earth Observation and Geoinformation, Vol. 30, pp. 139-145.   DOI
7 El-Askary, H., El-Mawla, S. H. Abd., Li, J., El-Hattab, M. M. and El-Raey, M., 2014, Change detection of coral reef habitat using landsat-5 TM, Landsat 7 ETM+ and Landsat 8 OLI data in the Red Sea (Hurghada, Egypt), International Journal of Remote Sensing, Vol. 35, No. 6, pp. 2327-2346.
8 Erdenechimeg, M., Choi, B., Na, Y. and Kim, T., 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. 28, No. 4, pp. 471-476.   과학기술학회마을
9 Jawak, S. D. and Luis, A. J. 2013, Very high-resolution satellite data for improved land cover extraction of larsemann hills, Eastern Antarctica, Journal of Applied Remote Sensing, Vol. 7, No. 1, pp. 1-28.
10 Jia, K., Wei, X., Gu, X., Yao, Y., Xie, X. and Li, B., 2014, Land cover classification using landsat 8 operational land imager data in beijing, China, Geocarto International, Vol. 29, No. 8, pp. 941-951.   DOI
11 Jiang, D., Huang, Y., Zhuang, D., Zhu, Y, Xu, X. and Ren, H., 2012, A simple semi-automatic approach for land cover classification from multispectral remote sensing imagery, PLOS ONE, Vol. 7, No. 9, pp. 1-10.
12 Jiang, H., Feng, M., Zhu, Y., Lu, N., Huang, J. and Xiao, T, 2014, An automated method for extracting rivers and lakes from landsat imagery, Remote Sensing, Vol. 6, No. 6, pp. 5067-5089.   DOI
13 Kim, B., Kim, Y., Han, Y., Choi, W. and Kim, Y., 2014, Fully automated generation of cloud-free imagery using landsat-8, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 32, No. 2, pp. 133-142.   과학기술학회마을   DOI
14 Kim, H. and Yeom, J., 2012, Effect of the urban land cover types on the surface temperature: case study of ilsan new city, Korean Journal of Remote Sensing, Vol. 28, No. 2, pp. 203-214.   과학기술학회마을   DOI
15 Kim, Y., Kim, Y., Park, W. and Eo, Y., 2010, Automated training from landsat image for classification of SPOT-5 and quickbird images, Korean Journal of Remote Sensing, Vol. 26, No.3, pp. 317-324.   과학기술학회마을   DOI
16 Park, H., Choi, J. and Choi, S. 2014, Impervious surface mapping of cheongju by using rapideye satellite imagery, Journal of the Korean Society for Geospatial Information System, Vol. 22, No. 1, pp. 71-79.   과학기술학회마을   DOI
17 Richter, R. and Schlapfer, D., 2012, Atmospheric/topographic correction for satellite imagery; ATCOR-2/3 user guide version, ReSe Applications Schlapfer, Wil, Switzerland.
18 Sanchez, S., Paz, A. and Plaza, A., 2011, A. real-time spectral unmixing using iterative error analysis on commodity graphics processing units. In proceedings, IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, BC, July, pp. 1767-1770.