Journal of Remote Sensing Technology
Journal of Remote Sensing Technology(JRST)
Frequency: Annually
Separability Analysis of Integrated Spaceborne Radar and Optical Data: Sudan Case Study
The purpose of this study was to determine via spectral separability using divergence measures the best individual and combinations of various numbers of bands for five land cover/ land use classes along the Blue Nile in Sudan. The data for this analysis were a stack of 15 layers including RADARSAT-2 C-band and PALSAR L-band quad-polarized radar registered with ASTER optical data, as well as four variance texture measures extracted from the RADARSAT-2 images. Spectral signatures were obtained for each class and examined by various separability measures. This examination is useful for better understanding the relative value of different types of remote sensing data and best band combinations for possible visual analysis and for improving land cover/ land use classification accuracy. Results show that the best single band for analysis was the RADARSAT-2 VH variance texture measure. The best pair of bands was the ASTER visible red and the RADARSAT-2 HV variance texture, which also included the PALSAR VH band for the best three band combination, all bands being very different data types. Further, based upon the divergence values, only eight bands are needed to achieve maximum separation between land cover/ land use classes. Beyond this point, classification accuracy is expected to decrease, with as few as six bands needed to reach viable classification accuracy.
Keywords:Separability; Divergence; RADARSAT-2; PALSAR; ASTER; Quad-polarization; Sensor Integration; Sensor Fusion; Texture; Sudan
Author: Barry Haack,Ron Mahabir


  1. J. Reiche, S. de Bruin, D. Hoekman, J. Verbesselt, and M. Herold, “A Bayesian approach to combine Landsat and ALOS PALSAR time series for near real-time deforestation detection,” Remote Sensing, vol. 7(5), pp. 4973-4996, 2015.
  2. H. Aghababaee, J. Amini, and Y.C. Tzeng, “Improving change detection methods of SAR images using fractals,” Scientia Iranica, vol. 20(1), pp. 15-22, 2013.
  3. F. Dell’Acqua, P. Gamba, and G. Lisini, “Improvements to urban area characterization using multitemporal and multiangle SAR images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 41(9), pp. 1996-2004, 2003.
  4. J. Toyra, A. Pietroniro, and L. Martz, “Multisensor hydrologic assessment of a freshwater wetland,” Remote Sensing of Environment, vol. 75(2), pp. 162-173, 2001.
  5. A. Sheoran and B. Haack, “Classification of California agriculture using quad polarization radar data and Landsat Thematic Mapper data,” GIScience & Remote Sensing, vol. 50(1), pp. 50-63, 2013.
  6. S. Sawaya, B. Haack, T. Idol, and A. Sheoran, “Land use/cover mapping with quad-polarization radar and derived texture measures near Wad Madani, Sudan,” GIScience & Remote Sensing, vol. 47(3), pp. 398-411, 2010.
  7. G. Forkuor, C. Conrad, M. Thiel, T. Ullmann, and E. Zoungrana, “Integration of optical and Synthetic Aperture Radar imagery for improving crop mapping in Northwestern Benin, West Africa,” Remote Sensing, vol. 6(7), pp. 6472-6499, 2014.
  8. S. Corgne, L. Hubert-Moy, and J. Betbeder, “Monitoring of agricultural landscapes using remote sensing data,” In: Land Surface Remote Sensing in Agriculture and Forest, N. Baghdadi, and M. Zribi, Eds., London, UK: ISTE Press Limited, pp. 221-248, 2016.
  9. N. Holah, N. Baghdadi, M. Zribi, A. Bruand, and C. King, “Potential of ASAR/ENVISAT for the characterization of soil surface parameters over bare agricultural fields,” Remote Sensing of Environment, vol. 96(1), pp. 78-86, 2005.
  10. J. Campbell and R. Wynne, Introduction to Remote Sensing, 5th ed., New York, NY: Guilford Press, 626 pp., 2012.
  11. H. McNairn and B. Brisco, “The application of C-band polarimetric SAR for agriculture: a review,” Canadian Journal of Remote Sensing, vol. 30(3), pp. 525-542, 2004.
  12. R.B. Smith, Interpreting digital radar images with TNTmips MicroImages Inc. (2012). The MicroImages website. [Online]. Available:
  13. S.T. Wu and S.A. Sader, “Multipolarization SAR data for surface feature delineation and forest vegetation characterization,” IEEE Transactions on Geoscience and Remote Sensing, vol. GE-25(1), pp. 67-76, 1987.
  14. T. Idol, B. Haack, and R. Mahabir, “Radar speckle reduction and derived texture measures for land cover/use classification: a case study,” Geocarto International, vol. 32(1), pp. 18-29, 2015.
  15. J.W. Cable, J.M. Kovacs, J. Shang, and X. Jiao, “Multi-temporal polarimetric RADARSAT-2 for land cover monitoring in Northeastern Ontario, Canada,” Remote Sensing, vol. 6(3), pp. 2372-2392, 2014.
  16. D. Bargiel and S. Herrmann, “Multi-temporal land-cover classification of agricultural areas in two European regions with high resolution spotlight TerraSAR-X data,” Remote Sensing, vol. 3(5), pp. 859-877, 2011.
  17. J.R. Otukei, T. Blaschke, and M. Collins, “Fusion of TerraSAR-x and Landsat ETM+ data for protected area mapping in Uganda,” International Journal of Applied Earth Observation and Geoinformation, vol. 38, pp. 99-104, 2015.
  18. X. Li and A.G. Yeh, “Multitemporal SAR images for monitoring cultivation systems using case-based reasoning,” Remote Sensing of Environment, vol. 90(4), pp. 524-534, 2004.
  19. T. Idol, B. Haack, and R. Mahabir, “Comparison and integration of spaceborne optical and radar data for mapping in Sudan,” International Journal of Remote Sensing, vol. 36(6), pp. 1551-1569, 2015.
  20. L.O. Pereira, C.C. Freitas, S.J.S. Sant’ Anna, D. Lu, and E.F. Moran, “Optical and radar data integration for land use and land cover mapping in the Brazilian Amazon,” GIScience & Remote Sensing, vol. 50(3), pp. 301-321, 2013.
  21. D. Amarsaikhan, M. Saandar, M. Ganzorig, H. H. Blotevogel, E. Egshiglen, R. Gantuyal, B. Nergui, and D. Enkhjargal, “Comparison of multisource image fusion methods and land cover classification,” International Journal of Remote Sensing, vol. 33(8), pp. 2532-2550, 2012.
  22. D. Lu, M. Batistella, and E. Moran, “Land cover classification in the Brazilian Amazon with the integration of Landsat ETM+ and RADARSAT data,” International Journal of Remote Sensing, vol. 28(24), pp. 5447-5459, 2007.
  23. D. Lu, P. Mausel, E. Brondizio, and E. Moran, “Change detection techniques,” International Journal of Remote Sensing, vol. 25(12), pp. 2365-2401, 2004.
  24. C. Sheffieled, “Selecting band combinations from multispectral data,” Photogrammetric Engineering and Remote Sensing, vol. 51(6), pp. 682-687, 1985.
  25. P.S. Chavez, S.C. Guptill, and J.A. Bowell, “Image processing techniques for Thematic Mapper Data,” Proceedings of ASPRS Technical Paper 50th Annual Meetings, vol. 2, pp. 728-742, 1984.
  26. M. Beauchemin and K.B. Fung, “On statistical band selection for image visualization,” Photogrammetric Engineering and Remote Sensing, vol. 67(5), pp. 571-574, 2001.
  27. E. Sarhrouni, A. Hammouch, and D. Aboutajdine, “Dimensionality reduction and classification feature using mutual information applied to hyperspectral images: a filter strategy based algorithm,” Applied Mathematical Sciences, vol. 6(102), pp. 5085-5095, 2012.
  28. M.C. Alonso, J.A. Malpica, and A.M. de Agirre, “Consequences of the hughes phenomenon on some classification Techniques,” ASPRS Annual Conference, Milwaukee, WI, pp. 9, 2011.
  29. F.O. Catak, and M.E. Balaban, “CloudSVM: Training an SVM classifier in cloud computing systems,” In: Pervasive Computing and the Networked World, Q. Zu, B. Hu, and A. Elci, Eds., New York, NY: Springer, pp. 57-69, 2013.
  30. A.M. Chandra and S.K. Ghosh, Remote Sensing and Geographical Information System, Oxford, UK: Alpha Science International Limited, pp. 79-97, 2006.
  31. P. H. Swain, T.V. Robertson, and A. G. Wacker, “Comparison of the Divergence and B-distance in Feature Selection,” The Laboratory for Applications of Remote Sensing, Report No. 020871, West Lafayette, IN, pp. 12, 1981.
  32. R. S. Latty and R. M. Hoffer, “Waveband evaluation of proposased Thermatic Mapper in forest cover classification,” Proceedings of the Fall Technical Meeting ACSM-ASP, Niagara Falls, NY, pp. RS-2-D-1 – 12, 1980.
  33. P. H. Swain and S. M. Davis, Eds., Remote Sensing: The Quantitative Approach. New York, NY: McGraw Hill, pp. 396, 1978.
  34. E.F. Moran, “Land cover classification in a complex urban-rural landscape with Quickbird imagery,” Photogrammetric Engineering and Remote Sensing, vol. 76, pp. 1159-1168, 2010.
  35. M. Joseph, S.R. Subramoniam, K.S. Srinivasan, S. Pathak, and J.R. Sharma, “Class separability analysis and classifier comparison using quad-polarization radar imagery,” Journal of the Indian Society of Remote Sensing, vol. 41, pp. 177-182, 2013.
  36. J. Reiche, R. Lucas, A.L. Mitchell, J. Verbesselt, D.H. Hoekman, J. Haarpaintner, J.M. Kellndorfer, A. Rosenqvist, E.A. Lehmann, C.E. Woodcock, and F.M. Seifert, “Combining satellite data for better tropical forest monitoring,” Nature Climate Change, vol. 6, pp. 120-122, 2016.
  37. NASA. The National Aeronautics and Space Administration website (2009). [Online]. Available:
  38. Canadian Space Agency. RADARSAT – 1. The Canadian Space Agency website (2008). [Online]. Available:
  39. JAXA. Image Data Acquired by the PALSAR Onboard the “Daichi”. The Japanese Aerospace Exploration Agency website (2006). [Online]. Available:
  40. R.M. Haralick, K. Shanmugam, and I.H. Dinstein, “Textural features for image classification,” Systems, Man and Cybernetics, IEEE Transactions, vol. 6. pp. 610-621, 1973.
  41. N. Herold, B. Haack, and E. Solomon, “An evaluation of RADAR texture for land use/cover extraction in varied landscapes,” International Journal of Applied Earth Observation and Geoinformation, vol. 5(2), pp. 113-128, 2004.
  42. M. Herold, X. Liu, and K. Clarke, “Spatial metrics and image texture for mapping urban land use,” Photogrammetric Engineering and Remote Sensing, vol. 69(9), pp. 991-1001, 2003.
  43. R.J. Dekker, “Texture analysis and classification of ERS SAR images for map updating of urban areas in The Netherlands,” IEEE Transactions on Geoscience and Remote Sensing, vol. 41(9), pp. 1950-1958, 2003.
  44. D.G. Leckie, “Forestry applications using imaging radar,” In: Manual of Remote Sensing: Principles and Applications of Imaging Radar, 3rd ed., F.M. Henderson, and A.J. Lewis, Eds., New York, NY: John Wiley and Sons, pp. 435-509, 1998.
  45. D.L. Evans, T.G. Farr, J.P. Ford, T.W. Thompson, and C.L. Werner, “Multipolarization radar images for geologic mapping and vegetation discrimination,” IEEE Transactions on Geoscience and Remote Sensing, vol. GE-24(2), pp. 246-257, 1986.
  46. Weather Underground. The Weather Underground website (2016). [Online]. Available:
  47. N. Joshi, M. Baumann, A. Ehammer, R. Fensholt, K. Grogan, P. Hostert, M.R. Jepsen, T. Kuemmerle, P. Mayfroidt, E.T.A. Mitchard, J. Reiche, C.M. Ryan, and B. Waske, “A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring,” Remote Sensing, vol. 8(1), pp. 70, 2016.