JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Automatic Extraction of Initial Training Data Using National Land Cover Map and Unsupervised Classification and Updating Land Cover Map
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Automatic Extraction of Initial Training Data Using National Land Cover Map and Unsupervised Classification and Updating Land Cover Map
Soungki, Lee; Seok Keun, Choi; Sintaek, Noh; Noyeol, Lim; Juweon, Choi;
  PDF(new window)
 Abstract
Those land cover maps have widely been used in various fields, such as environmental studies, military strategies as well as in decision-makings. This study proposes a method to extract training data, automatically and classify the cover using ingle satellite images and national land cover maps, provided by the Ministry of Environment. For this purpose, as the initial training data, those three were used; the unsupervised classification, the ISODATA, and the existing land cover maps. The class was classified and named automatically using the class information in the existing land cover maps to overcome the difficulty in selecting classification by each class and in naming class by the unsupervised classification; so as achieve difficulty in selecting the training data in supervised classification. The extracted initial training data were utilized as the training data of MLC for the land cover classification of target satellite images, which increase the accuracy of unsupervised classification. Finally, the land cover maps could be extracted from updated training data that has been applied by an iterative method. Also, in order to reduce salt and pepper occurring in the pixel classification method, the MRF was applied in each repeated phase to enhance the accuracy of classification. It was verified quantitatively and visually that the proposed method could effectively generate the land cover maps.
 Keywords
Land Cover Map;Training Data;MRF;Medium-resolution Satellite Image;
 Language
Korean
 Cited by
1.
Landsat 위성영상을 활용한 낙동강 삼각주 연안사주의 면적 시계열 분석,이슬기;양미희;이창욱;

한국측량학회지, 2016. vol.34. 5, pp.457-469 crossref(new window)
 References
1.
Blaschke, T. (2010), Object based image analysis for remote sensing, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 65, No. 1, pp. 2-16. crossref(new window)

2.
Bruzzone, L. and Prieto, D. F. (2000), Automatic analysis of the difference image for unsupervised change detection, IEEE Transactions on Geoscience and Remote Sensing, Vol. 38, No. 3, pp. 1171-1182. crossref(new window)

3.
Chen, X., Chen, J., Shi, Y., and Yamaguchi, Y. (2012), An automated approach for updating land cover maps based on integrated change detection and classification methods, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 71, pp. 86-95. crossref(new window)

4.
Choi, J. W., Noh, S. T., and Choi, S. K. (2014a), Unsupervised classification of Landsat-8 OLI satellite imagery based on iterative spectral mixture model, Journal of the Korean Society for Geospatial Information System, Vol. 22, No. 4, pp. 53-61. (in Korean with English abstract)

5.
Choi, S. K., Lee, S. K., and Wang, B. (2014b), 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. (in Korean with English abstract) crossref(new window)

6.
Dickinson, R. E., Kennedy, P. J., and Henderson-Sellers, A. (1993), Biosphere-atmosphere transfer scheme (BATS) version 1e as coupled to the NCAR community climate model, NCAR/TN-387+STR, National Center for Atmospheric Research, pp. 1-59.

7.
Franklin, S. E. and Wulder, M. A. (2002), Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas, Progress in Physical Geography, Vol. 26, No. 2, pp. 173-205. crossref(new window)

8.
Friedl, M. A. and Brodley, C. E. (1997), Decision tree classification of land cover from remotely sensed data, Remote Sensing of Environment, Vol. 61, No. 3, pp. 399-409. crossref(new window)

9.
Friedl, M. A., McIver, D. K., Hodges, J. C., Zhang, X. Y., Muchoney, D., Strahler, A. H., Woodcock. C. E., Gopal. S., Schneider. A., Cooper. A., Baccini. a., Gao. F., and Schaaf, C. (2002), Global land cover mapping from MODIS: algorithms and early results, Remote Sensing of Environment, Vol. 83, No. 1, pp. 287-302. crossref(new window)

10.
Hansen, M. C., DeFries, R. S., Townshend, J. R., and Sohlberg, R. (2000), Global land cover classification at 1 km spatial resolution using a classification tree approach, International Journal of Remote Sensing, Vol. 21, No. 6-7, pp. 1331-1364. crossref(new window)

11.
Hansen, M. C. and Reed, B. (2000), A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products, International Journal of Remote Sensing, Vol. 21, No. 6-7, pp. 1365-1373. crossref(new window)

12.
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. crossref(new window)

13.
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.

14.
Jensen, J. R. (2009), Remote Sensing of the Environment: An Earth Resource Perspective(2nd Edition), Prentice Hall Inc.

15.
KaptuéTchuenté, A. T., Roujean, J. L., Bégué, A., Los, S. O., Boone, A. A., Mahfouf, J. F., Carrer, D., and Daouda, B. (2011), A new characterization of the land surface heterogeneity over Africa for use in land surface models, Journal of Hydrometeorology, Vol. 12. No. 6, pp. 1321-1336. crossref(new window)

16.
Kim, Y. M., Kim, Y. I., Park, W. Y., and Eo, Y. D. (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.

17.
Loveland, T. R., Reed, B. C., Brown, J. F., Ohlen, D. O., Zhu, Z., Yang, L. W. M. J., and Merchant, J. W. (2000), Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data, International Journal of Remote Sensing, Vol. 21, No. 6-7, pp. 1303-1330. crossref(new window)

18.
Lu, D., Mausel, P., Brondizio, E., and Moran, E. (2004), Change detection techniques, International Journal of Remote Sensing, Vol. 25. No. 12, pp. 2365-2401. crossref(new window)

19.
Lu, D. and Weng, Q. (2007), A survey of image classification methods and techniques for improving classification performance, International Journal of Remote Sensing, Vol. 28, No. 5, pp. 823-870. crossref(new window)

20.
Pal, M. and Mather, P. M. (2003), An assessment of the effectiveness of decision tree methods for land cover classification, Remote Sensing of Environment, Vol. 86, No. 4, pp. 554-565. crossref(new window)

21.
Richter, R. and Schläpfer, D. (2005), Atmospheric/Topographic Correction for Satellite Imagery, DLR report DLR-IB, pp. 565-01.

22.
Schläpfer, D., Richter, R., and Kellenberger, T. W. (2012), Aspects of atmospheric and topographic correction of high spatial resolution imagery, In IGARSS, pp. 4291-4294.

23.
Selim, S. Z. and Ismail, M. A. (1984), K-means-type algorithms: a generalized convergence theorem and characterization of local optimality, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 1, pp. 81-87.

24.
Sui, H., Peng, F., Xu, C., Sun, K., and Gong, J. (2012), GPU-accelerated MRF segmentation algorithm for SAR images, Computers and Geosciences, Vol. 43, pp. 159-166. crossref(new window)

25.
Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M., and Rother, C. (2008), A comparative study of energy minimization methods for markov random fields with smoothness-based priors, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30, No. 6, pp. 1068-1080. crossref(new window)

26.
Yu, L., Porwal, A., Holden, E. J., and Dentith, M. C. (2012), Towards automatic lithological classification from remote sensing data using support vector machines, Computers and Geosciences, Vol. 45, pp. 229-239. crossref(new window)

27.
Zhang, Z., Wang, X., Zhao, X., Liu, B., Yi, L., Zuo, L., Wen, Q., Liu, F., Xu, J., and Hu, S. (2014), A 2010 update of national land use/cover database of China at 1:100000 scale using medium spatial resolution satellite images, Remote Sensing of Environment, Vol. 149, pp. 142-154. crossref(new window)