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AUTOMATIC WATER BODY EXTRACTION FROM REMOTE SENSING IMAGES USING ENTROPY

Aahlen, Julia, Seipel, Stefan

First published: 2015https://doi.org/10.5593/sgem2015/b21/s8.064View metrics

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Publication details

Title
AUTOMATIC WATER BODY EXTRACTION FROM REMOTE SENSING IMAGES USING ENTROPY
Authors
Aahlen, Julia, Seipel, Stefan
Proceedings
SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings; 15th International Multidisciplinary Scientific GeoConference SGEM2015, INFORMATICS, GEOINFORMATICS AND REMOTE SENSING
Publisher
Stef92 Technology
Year
2015
Pages
517-524
ISSN
1314-2704
ISBN
978-619-7105-34-6
Language
en
Publication type
Conference Paper
References32

  1. Vericat D, Brassington J, Wheaton J, Cowie M. Accuracy assessment of aerial photographs acquired using lighter-than-air blimps: low cost tools for mapping river corridors. Journal of River Research and Applications, vol. 25(8), 2009, pp 985-1000.

  2. Mckay, P, & Blain, C.A. An Automated Approach to Extracting River Bank Locations From Aerial Imagery Using Image Texture. Journal of River Research and Applications, vol.30, 2014, pp 1048– 1055.

  3. Campos, J. C., Sillero, N., & Brito, J. C. Normalized difference water indexes have dissimilar performances in detecting seasonal and permanent water in the Sahara-Sahel transition zone. Journal Of Hydrology, vol. 464 –465, 2012, pp 438- 446.

  4. Jiang, H., Feng M., Zhu,Y., Lu, N., Huang, J. and Xiao, T. An Automated Method for Extracting Rivers and Lakes from Landsat Imagery, Journal of Remote Sensing, vol. 6, 2014, pp 5067-5089. International Multidisciplinary Scientific GeoConfenferences SGEM 2015 www.sgem.org 15th International Multidisciplinary Scientific GeoConferences SGEM2015

  5. Xi, J., Zhang, Ji-Zhong, Edge Detection from Remote Sensing Images Based on Canny Operator and Hough Transform. Advances in Intelligent and Soft Computing, vol. 141, 2012, pp 807-814.

  6. Kmiec, M. Object detection in security applications using dominant edge directions. Pattern Recognition Letters. vol. 52, 2015, pp 72 – 79.

  7. Shimada, T., Sakaida, F., Kawamura, H. & Okumura, T. Application of an edge detection method to satellite images for distinguishing sea surface temperature fronts near the Japanese coast. Remote Sensing of Environment, vol 98, issue 1, 2005, pp 21-34.

  8. Zhao J., Heng Y., Gu X. & Wang S. The edge detection of river model based on self-adaptive Canny Algorithm and Connected Domain Segmentation. 8th World Congress on Intelligent Control and Automation (WCICA), China, 2010, pp 1333 – 1336.

  9. Åhlén, J. & Seipel, S. Time-Space visualisation of Amur River channel changes due to flooding disaster . 14th SGEM GeoConference on Informatics, Geoinformatics and Remote Sensing, www.sgem.org, SGEM2014 Conference Proceedings, ISBN 978-619 -7105-10-0 / ISSN 1314-2704 , 2014, v ol. 1, pp 873- 882.

  10. Liu, Y., Why NDWI threshold varies in delineating water body from multitemporal images Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International, 2012, pp 4375-4378

  11. Dai. W., Wang, K. An Image Edge Detection Algorithm Based on Local Entropy, IEEE International Conference, 2 nd International Conference on Integration Technology, 2007, pp nil1 - nil5.

  12. Barbieri, A.L., de Arruda G.F., Rodrigues, F. A., Brunoa, O. M., Fontoura Costa, L. An entropy-based approach to automatic image segmentation of satellite images, Physica A, vol. 390, 2011, pp 512–518.

  13. FRA Remote Sensing Portal, URL: http://www.fao.org/forestry/fra/remotesensing/en/, last access April 2015.

  14. ©Lantmäteriet [I2014/00655], URL: http://maps.slu.se, last access April 2015.

  15. Buckland, M.K., Gey, F.C.,. The relationship between recall and precision. J. Am. Soc. Inform. Sci. vol. 45, 1994, pp 12–19.

  16. Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A., The pascal visual object classes (voc) challenge. Int. J. Comput. Vision, vol. 88, 2010, pp 303-338. International Multidisciplinary Scientific GeoConfenferences SGEM 2015 www.sgem.org

  17. Vericat D, Brassington J, Wheaton J, Cowie M. Accuracy assessment of aerial photographs acquired using lighter-than-air blimps: low cost tools for mapping river corridors. Journal of River Research and Applications, vol. 25(8), 2009, pp 985-1000.

  18. Mckay, P, & Blain, C.A. An Automated Approach to Extracting River Bank Locations From Aerial Imagery Using Image Texture. Journal of River Research and Applications, vol.30, 2014, pp 1048– 1055.

  19. Campos, J. C., Sillero, N., & Brito, J. C. Normalized difference water indexes have dissimilar performances in detecting seasonal and permanent water in the Sahara-Sahel transition zone. Journal Of Hydrology, vol. 464 –465, 2012, pp 438- 446.

  20. Jiang, H., Feng M., Zhu,Y., Lu, N., Huang, J. and Xiao, T. An Automated Method for Extracting Rivers and Lakes from Landsat Imagery, Journal of Remote Sensing, vol. 6, 2014, pp 5067-5089. International Multidisciplinary Scientific GeoConfenferences SGEM 2015 www.sgem.org 15th International Multidisciplinary Scientific GeoConferences SGEM2015

  21. Xi, J., Zhang, Ji-Zhong, Edge Detection from Remote Sensing Images Based on Canny Operator and Hough Transform. Advances in Intelligent and Soft Computing, vol. 141, 2012, pp 807-814.

  22. Kmiec, M. Object detection in security applications using dominant edge directions. Pattern Recognition Letters. vol. 52, 2015, pp 72 – 79.

  23. Shimada, T., Sakaida, F., Kawamura, H. & Okumura, T. Application of an edge detection method to satellite images for distinguishing sea surface temperature fronts near the Japanese coast. Remote Sensing of Environment, vol 98, issue 1, 2005, pp 21-34.

  24. Zhao J., Heng Y., Gu X. & Wang S. The edge detection of river model based on self-adaptive Canny Algorithm and Connected Domain Segmentation. 8th World Congress on Intelligent Control and Automation (WCICA), China, 2010, pp 1333 – 1336.

  25. Åhlén, J. & Seipel, S. Time-Space visualisation of Amur River channel changes due to flooding disaster . 14th SGEM GeoConference on Informatics, Geoinformatics and Remote Sensing, www.sgem.org, SGEM2014 Conference Proceedings, ISBN 978-619 -7105-10-0 / ISSN 1314-2704 , 2014, v ol. 1, pp 873- 882.

  26. Liu, Y., Why NDWI threshold varies in delineating water body from multitemporal images Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International, 2012, pp 4375-4378

  27. Dai. W., Wang, K. An Image Edge Detection Algorithm Based on Local Entropy, IEEE International Conference, 2 nd International Conference on Integration Technology, 2007, pp nil1 - nil5.

  28. Barbieri, A.L., de Arruda G.F., Rodrigues, F. A., Brunoa, O. M., Fontoura Costa, L. An entropy-based approach to automatic image segmentation of satellite images, Physica A, vol. 390, 2011, pp 512–518.

  29. FRA Remote Sensing Portal, URL: http://www.fao.org/forestry/fra/remotesensing/en/, last access April 2015.

  30. ©Lantmäteriet [I2014/00655], URL: http://maps.slu.se, last access April 2015.

  31. Buckland, M.K., Gey, F.C.,. The relationship between recall and precision. J. Am. Soc. Inform. Sci. vol. 45, 1994, pp 12–19.

  32. Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A., The pascal visual object classes (voc) challenge. Int. J. Comput. Vision, vol. 88, 2010, pp 303-338. International Multidisciplinary Scientific GeoConfenferences SGEM 2015 www.sgem.org

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