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2012-03-26
Jute and Tea Discrimination through Fusion of SAR and Optical Data
By
Progress In Electromagnetics Research B, Vol. 39, 337-354, 2012
Abstract
Remote sensing approaches based on both optical and microwave region of EM spectra have been widely adapted for large scale crop monitoring and condition assessment. Visible, infrared and microwave wavelengths are sensitive to different crop characteristics, thus data from optical and radar sensors are complementary. Synthetic Aperture Radar (SAR) responds to the large scale crop structure (size, shape and orientation of leaves, stalks, and fruits) and the dielectric properties of the crop canopy. Research is needed to assess the saturation effects of SAR data and to investigate the synergy between the optical and SAR imagery for exploring various dimensions of crop growth which is not possible with any one of them singly with higher degree of accuracy. An attempt has been made to study the potential of SAR and optical data individually and by fusing them to separate various landcover classes. Two-date and three-date SAR data could distinguish jute and tea crop with 70-85% accuracy, while cloud free optical data (green, red and infrared bands) resulted in accuracy 80-85%. On fusing the optical and SAR single date data of May, 29 2010 using Brovey method, an accuracy of 85{\%} was obtained. PCA and HSV with munsell based approaches resulted in similar accuracies but HSV performed the best among these. This emphasizes on the synergistic effect of SAR and optical data. Also the fused data could be used to delineate the crop condition and age by inputs like NDVI from optical and XPR (Cross polarization ratio) from SAR data. The co- and cross polarization ratios along with various indices viz. Biomass Index (BMI), Volume Scattering Index (VSI) and canopy structural index (CSI) were used to discriminate tea from jute. Due to differences in structural component of tea and jute at early season as manifested by the indices, there is clear separability as observed from the mean values. Among the dual polarization combinations, HV/VV performed the best (70%) followed by HV/HH (62%) and lastly HH/VV (42%). Among the single best indices for discrimination BMI performed the best. Combination of Co, Cross-polarization and BMI yields around 80% classification accuracy. BMI and VSI combination yielded the best classification accuracy of 84%. This level of accuracy obtained was much superior to that of multidate HH polarization SAR data.
Citation
Dipanwita Haldar, Chakrapani Patnaik, Shiv Mohan, and Manab Chakraborty, "Jute and Tea Discrimination through Fusion of SAR and Optical Data," Progress In Electromagnetics Research B, Vol. 39, 337-354, 2012.
doi:10.2528/PIERB11123011
References

1. Chang, C. I., Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Kluwer Academic, New York, 2003.

2. Colditz, R. R., T. Wehrmann, M. Bachmann, K. Steinnocher, M. Schmidt, G. Strunz, and S. Dech, "Influence of imagefusion approaches on classification accuracy --- A case study," International Journal of Remote Sensing, Vol. 27, No. 15, 3311-3335, 2006.
doi:10.1080/01431160600649254

3. Dobson, M. C., F. T. Ulaby, and L. E. Pierce, "Land-cover classification and estimation of terrain attributes using synthetic aperture radar," Remote Sensing of Environment, Vol. 51, 199-214, 1995.
doi:10.1016/0034-4257(94)00075-X

4. Horgan, G. W., C. Glasbey, J. N. Cuevas Gozalo, S. L. Soria, and F. G. Alonso, "Land-use classification in central Spain using SIR-A and MSS imagery," International Journal of Remote Sensing, Vol. 13, No. 15, 2839-2848, 1992.
doi:10.1080/01431169208904085

5. Sandholt, I., "The combination of polarimetric SAR with satellite SAR and optical data for classification of agricultural land," Geografisk Tidsskrift, Danish Journal of Geography, Vol. 101, 21-32, 1995.

6. Kumar, A. S., B. Kartikeyan, and K. L. Majumder, "Band sharpening of IRS multispectral imagery by cubic spline wavelets," International Journal of Remote Sensing, Vol. 21, 581-594, 2000.
doi:10.1080/014311600210768

7. Lobell, D. B. and G. P. Asner, "Cropland distributions from temporal unmixing of MODIS data," Remote Sensing of Environment, Vol. 93, No. 3, 412-422, 2004.
doi:10.1016/j.rse.2004.08.002

8. Munechika, C. K., J. S. Warnick, C. Salvaggio, and J. R. Schott, "Resolution enhancement of multispectral image data to improve classification accuracy," Photogrammetric Engineering and Remote Sensing, Vol. 59, No. 1, 67-72, 1993.

9. Pohl, C. and J. L. Van Genderen, "Multisensor image fusion in remote sensing: Concepts, methods and applications," International Journal of Remote Sensing, Vol. 19, 823-854, 1998.
doi:10.1080/014311698215748

10. Pope, K. O, J. M. Rey-Benayas, and J. F. Paris, "Radar remote sensing of forest and wetland ecosystems in the central american tropics," Remote Sensing of Environment, Vol. 48, 205-219, 1994.
doi:10.1016/0034-4257(94)90142-2

11. Prinz, B., R. Wiemker, and H. Spitzer, "Simulation of high resolution satellite imagery form multispectral airborne scanner imagery for accuracy assessment of fusion algorithms," Proceedings of the ISPRS Joint Workshop `Sensors and Mapping form Space' of Working Group I/1, I/3 and IV/4, Hannover, Germany, October 1997.

12. Ranchin, T. and L. Wald, "Fusion of high spatial and spectral resolution images: The ARSIS concept and its implementation," Photogrammetric Engineering and Remote Sensing, Vol. 66, 49-61, 2000.

13. Raptis, V. S., R. A. Vaughan, I. N. Hatzopolous, and V. Papapanagiotou, "The use of data fusion for the classification of dense urban environments, the Mytilene case," Future Trends in Remote Sensing, 427-433, Edited by P. Gudmandsen (Rotterdam: Balkema), 1998.

14. Ray, S. S., "Merging of IRS LISS III and PAN data --- Evaluation of various methods for a predominantly agricultural area," Int. J. Remote Sensing, Vol. 25, No. 13, 2657-2664, July 10, 2004.
doi:10.1080/01431160410001665821

15. Sandholt, I., B. Fog, J. N. Poulsen, M. Stjernholm, and H. Skriver, "Classification of agricultural crops in Denmark using ERS-1 SAR and SPOT imagery," Sensors and Environmental Applications of Remote Sensing, Proccedings of the 14th EARSeL Symposium, Askne, J., editor, 37--44, A.A. Balkema Publishers, Rotterdam, Goteborg, Sweden, June 6--8, 1994.

16. Shaban, M. A. and O. Dikshit, "Evaluation of the merging of SPOT multispectral and panchromatic data for classification of an urban environment," International Journal of Remote Sensing, Vol. 23, No. 2, 249-262, 2002.
doi:10.1080/01431160010007088

17. Solberg, A. H. S., A. K. Jain, and T. Taxt, "Multisource classification of remotely sensed data: Fusion of Landsat TM and SAR images," IEEE Transactions on Geoscience and Remote Sensing, Vol. 32, No. 4, 768-785, 1994.
doi:10.1109/36.298006

18. Solberg, A. H. S., T. Taxt, and A. K. Jain, "A markov random field model for classification of multisource satellite imagery," IEEE Transactions on Geoscience and Remote Sensing, Vol. 34, No. 1, 100-113, 1996.
doi:10.1109/36.481897

19. Terrettaz, P., "Comparison of different methods to merge SPOT P and XS data: Evaluation in an urban area," Future Trends in Remote Sensing, Edited by P. Gudmandsen, 435--443, Rotterdam, Balkema, 1998.

20. Van Niel, T. G. and T. R. McVicar, "Remote sensing of ricebased irrigated agriculture: A review," Cooperative Research Centre for Sustainable Rice Production, P1105-01/01, Yanco, NSW, Australia, 2001.

21. Wald, L., T. Ranchin, and M. Mangoloni, "Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images," Photogrammetric Engineering and Remote Sensing, Vol. 63, 691-699, 1997.

22. Zhang, Y., "Problems in the fusion of commercial high-resolution satellites images as well as LANDSAT 7 images and initial solutions," Proceedings of the ISPRS, CIG, and SDH Joint International Symposium on Geospatial Theory, Processing and Applications, Ottawa, Canada, unpaginated CD-ROM, July 9--12, 2002.

23. Zhang, Y., "Understanding image fusion," Photogrammetric Engineering and Remote Sensing, Vol. 66, No. 1, 49-61, 2004.