Computerized Seed and Range Selection Method for Flood Extent Extraction in SAR Image Using Iterative Region Growing
- 12 Downloads
This study presents a novel method to capture the flood-affected area in SAR image. It initially locates a pixel in HH-polarized SAR image whose intensity value is equal or close to minimum intensity value in that image. This is adapted since SAR reflectance values of flooded area are less than the other regions due to the water surface smoothness that makes the flood surface a specular reflector with nearly no return to the sensor. Thereafter, the identified seed point is confirmed locally based on two parameters corresponding to intensities and percentage of occurrence of intensities around the seed. Densely populated range around the seed point is computed in the second step. Subsequently in the third step, from the seed point, regions are grown till the intensity value of that point is within the range. These three steps are continued till all flooded regions are captured in SAR image. The algorithm works with minimum human interaction. This method is validated by applying on RADARSAT-2 data and is found that the classification accuracy is 95%, in comparison with “mean shift” and “LPQ”.
KeywordsSynthetic-aperture radar Flood area extraction Seed Region growing Clustering
Authors are thankful to the Director, NRSC, Hyderabad, India, and the CGM, RCs, NRSC for their support and guidance during the course of this study. Authors sincerely thank the anonymous reviewers for contributing insightful remarks and useful suggestions that have substantially improved the quality of the manuscript. Authors gratefully acknowledge the GM, RRSC-East, NRSC, Kolkata, India, and Head (Applications), RRSC-East, NRSC, Kolkata, India, for giving their continuous support and guidance during this study.
- Canada Centre for Remote Sensing (CCRS). (2011). Advance radar polarimetric tutorial. http://gs.mdacorporation.com/SatelliteData/Radarsat2/Radarsat2.aspx. Accessed 3 June 2016.
- Chakraborty, D., Sen, G. K., & Hazra, S. (2012). Image segmentation Techniques (pp. 1–128). Saarbrücken: LAP LAMBERT Academic Publishing.Google Scholar
- Chakraborty, D., Sen, G. K., Hazra, S., & Jeyaram, A. (2008). Clustering for high resolution monochrome satellite image segmentation. International Journal of Geoinformatics, 4(1), 1–9.Google Scholar
- Chunming, H., Huadong, G., Shao, Y., & Liao, J. (2005). Detection of the flood boundary in SAR image using texture. In IEEE geoscience and remote sensing symposium.Google Scholar
- e-Geos (2013). COSMO-SkyMed system description and user guide. Rome, Italy.Google Scholar
- Herrera-Cruz, V., & Koudogbo, F. (2009). TerraSAR-X rapid mapping for flood events. In Proceedings of international society for photogrammetry and remote sensing (earth imaging for geospatial information), Hannover, Germany (pp. 170–175).Google Scholar
- Hillman, A., Rolland, P., Periard, R., Luscombe, A., Chabot, M., Chen, C., et al. (2009). RADARSAT-2 initial system operations and performance. In Proceedings of IEEE IGARSS (Vol. 2, pp. 753–756).Google Scholar
- Jenson, J. R. (2011). Remote sensing of the environment—An earth resource perspective (2nd ed.). New Delhi: Pearson Education.Google Scholar
- Joseph, G. (2013). Fundamental of remote sensing (2nd ed.). Hyderabad: Universities Press.Google Scholar
- Lopes, A., Nezry, E., Touzi, R., & Laur, H. (1990). Maximum a posteriori speckle filtering and first order texture models in SAR Images. In 10th annual international symposium on geoscience and remote sensing (pp. 2409–2412).Google Scholar
- Misra, T., Rana, S. S., Desai, N. M., Dave, D. B., Rajeevjyoti, A. R. K., Rao, C. V. N., et al. (2013). Synthetic aperture radar payload on-board RISAT-1: Configuration, technology and performance. Current Science, 104(4), 446–461.Google Scholar
- Panetti, A., L’Abbate, M., Bruno, C., Bauleo, A., Catalano, T., Cotogni, M., et al. (2014). Sentinel-1 spacecraft. In Proceedings of EUSAR, Berlin, Germany (pp. 4–7).Google Scholar
- Selvi, C., & Sathya, S. (2014). Flood identification using satellite images. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 3(1), 6497–6504.Google Scholar
- Van der Sande, C. J., de Jong, S. M., & de Roo, A. P. J. (2003). A segmentation and classification approach of IKONOS-2 imagery for land cover mapping to assist flood risk and flood damage assessment. International Journal of Applied Earth Observation and Geoinformation, 4, 217–229.CrossRefGoogle Scholar
- Yu, Q., & Clausi, D. A. (2006). Joint image segmentation and interpretation using iterative semantic region growing on SAR sea ice imagery. In 18th international conference on pattern recognition (ICPR) (pp. 223–226).Google Scholar