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RIPARIAN LANDCOVER CLASSIFICATION USING HIGH RESOLUTION IMAGERY AND OOC TECHNIQUE

Jeong, Seunggyu, Seo, . Chang-Wan, Park, essor. Chong-Hwa

First published: 2013-06-20https://doi.org/10.5593/sgem2013/bb2.v2/s10.018View metrics

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  • Citations
  • CrossRef - Citation Indexes: 1
  • Scopus - Citation Indexes: 1
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  • Mendeley - Readers: 8

Publication details

Title
RIPARIAN LANDCOVER CLASSIFICATION USING HIGH RESOLUTION IMAGERY AND OOC TECHNIQUE
Authors
Jeong, Seunggyu, Seo, . Chang-Wan, Park, essor. Chong-Hwa
Proceedings
SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings; 13th SGEM GeoConference on INFORMATICS, GEOINFORMATICS AND REMOTE SENSING
Publisher
Stef92 Technology
Year
2013
Pages
679 - 686 pp
ISSN
1314-2704
ISBN
Not available yet
Language
en
Publication type
Conference Paper
References20
  1. Qian, J., Zhou, Q., & Hou, Q. (2000). COMPARISON OF PIXEL-BASED AND OBJECT- ORIENTED CLASSIFICATION METHODS FOR EXTRACTING BUILT-UP AREAS IN ARIDZONE. Image (Rochester, N.Y.), 8(August)

  2. Dave Vanderzanden, (2002). High Resolution Image Classification: A Forest Service Test of Visual Learning System’s Feature Analyst.

  3. John R. Jensen. (2005) Introductory Digital Image Processing: A Remote Sensing Perspective, 495-513

  4. Lillesand, T.M., Kiefer, R.W., & Chipman, J.W. (2004) Remote Sensing and Image Interpretation. Fourth Edition. New York: Wiley

  5. Newell, J., & Olofsson, P. (n.d.). eCognition Tutorial : Classifying a QuickBird Image, 1 – 22.

  6. Ag, D. (2007). Definiens Developer 7 Reference Book.

  7. Macdonald, L. (2008). Using LIDAR data with ArcGIS.

  8. Herold, M., Goldstein, N.C., and Clarke, K.C., 2003. The spatiotemporal form of urban growth: measurement, analysis and modeling. Remote Sensing of Environment, 86, pp. 286-302.

  9. Hollaus, M., Wagner, W., Eberhöfer, C. and Karel, W., 2006. Accuracy of large-scale canopy heights derived from LiDAR data under operational constraints in a complex alpine environment. ISPRS Journal of Photogrammetry & Remote Sensing 60(5), pp. 323-338.

  10. Baatz, M., Benz, U., Dehghani, S., Heynen, M., Höltje, A., Hofmann, P., Lingenfelder, I.,Mimler, M., Sohlbach, M., Weber, M., Willhauck, G., 2004. eCognition Professional 4.0 User Guide. Definiens Imaging GmbH, Munich, Germany.

  11. Qian, J., Zhou, Q., & Hou, Q. (2000). COMPARISON OF PIXEL-BASED AND OBJECT- ORIENTED CLASSIFICATION METHODS FOR EXTRACTING BUILT-UP AREAS IN ARIDZONE. Image (Rochester, N.Y.), 8(August)

  12. Dave Vanderzanden, (2002). High Resolution Image Classification: A Forest Service Test of Visual Learning System’s Feature Analyst.

  13. John R. Jensen. (2005) Introductory Digital Image Processing: A Remote Sensing Perspective, 495-513

  14. Lillesand, T.M., Kiefer, R.W., & Chipman, J.W. (2004) Remote Sensing and Image Interpretation. Fourth Edition. New York: Wiley

  15. Newell, J., & Olofsson, P. (n.d.). eCognition Tutorial : Classifying a QuickBird Image, 1 – 22.

  16. Ag, D. (2007). Definiens Developer 7 Reference Book.

  17. Macdonald, L. (2008). Using LIDAR data with ArcGIS.

  18. Herold, M., Goldstein, N.C., and Clarke, K.C., 2003. The spatiotemporal form of urban growth: measurement, analysis and modeling. Remote Sensing of Environment, 86, pp. 286-302.

  19. Hollaus, M., Wagner, W., Eberhöfer, C. and Karel, W., 2006. Accuracy of large-scale canopy heights derived from LiDAR data under operational constraints in a complex alpine environment. ISPRS Journal of Photogrammetry & Remote Sensing 60(5), pp. 323-338.

  20. Baatz, M., Benz, U., Dehghani, S., Heynen, M., Höltje, A., Hofmann, P., Lingenfelder, I.,Mimler, M., Sohlbach, M., Weber, M., Willhauck, G., 2004. eCognition Professional 4.0 User Guide. Definiens Imaging GmbH, Munich, Germany.

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Number of times cited according to Crossref: 1

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