Expansion of LISS III swath using AWiFS wider swath data and contourlet coefficients learning

References

• Auernhammer, H. 2001. “Precision Farming—The Environmental Challenge.” Computers and Electronics in Agriculture 30: 31–43. doi:10.1016/S0168-1699(00)00153-8.
   Web of Science ®Google Scholar
• Baker, S., and T. Kanade. 2002. “Limits on Super-Resolution and How to Break Them.” IEEE Transactions on Pattern Analysis and Machine Intelligence 24: 1167–1183. doi:10.1109/TPAMI.2002.1033210.
   Web of Science ®Google Scholar
• Chander, G., M. J. Coan, and P. L. Scaramuzza. 2008. “Evaluation and Comparison of the IRS-P6 and the Landsat Sensors.” IEEE Transactions on Geoscience and Remote Sensing 46: 209–221. doi:10.1109/TGRS.2007.907426.
   Web of Science ®Google Scholar
• Chang, C. C., and C. J. Lin. 2001. “LIBSVM: A Library for Support Vector Machines.” Accessed 30 June, 2014. http://www.csie.ntu.edu.tw/~cjlin/libsvm.
   Google Scholar
• Chang, H., D. Y. Yeung, and Y. Xiong. 2004. “Super-Resolution through Neighbor Embedding.” Proceedings IEEE Conference CVPR 1: I-275–I-282.
   Google Scholar
• Coops, N. C., M. Johnson, M. A. Wulder, and J. C. White. 2006. “Assessment of QuickBird High Spatial Resolution Imagery to Detect Red Attack Damage Due to Mountain Pine Beetle Infestation.” Remote Sensing of Environment 103: 67–80. doi:10.1016/j.rse.2006.03.012.
   Web of Science ®Google Scholar
• Da Cunha, A. L., J. Zhou, and M. N. Do. 2006. “The Nonsubsampled Contourlet Transform: Theory, Design, and Applications.” IEEE Transactions on Image Processing 15: 3089–3101. doi:10.1109/TIP.2006.877507.
   PubMed Web of Science ®Google Scholar
• Do, M. N., and M. Vetterli. 2005. “The Contourlet Transform: An Efficient Directional Multiresolution Image Representation.” IEEE Transactions on Image Processing 14: 2091–2106. doi:10.1109/TIP.2005.859376.
   PubMed Web of Science ®Google Scholar
• Drucker, H., C. J. C. Burges, L. Kauffman, A. Smola, and V. Vapnik. 1997. “Support Vector Regression Machines.” In Neural Information Processing Systems 9, edited by M. C. Mozer, J. I. Joradn, and T. Petsche, 155–161. Cambridge, MA: MIT Press.
   Google Scholar
• Elad, M., and D. Datsenko. 2009. “Example-Based Regularization Deployed to Super-Resolution Reconstruction of a Single Image.” The Computer Journal 52: 15–30. doi:10.1093/comjnl/bxm008.
   Web of Science ®Google Scholar
• Freeman, W. T., T. R. Jones, and E. C. Pasztor. 2002. “Example-Based Superresolution.” IEEE Computer Graphics and Applications 22: 56–65. doi:10.1109/38.988747.
   Web of Science ®Google Scholar
• Gillespie, T. W., G. M. Foody, D. Rocchini, A. P. Giorgi, and S. Saatchi. 2008. “Measuring and Modelling Biodiversity from Space.” Progress in Physical Geography 32: 203–221. doi:10.1177/0309133308093606.
   Web of Science ®Google Scholar
• Goward, S. N., G. Chander, M. Pagnutti, A. Marx, R. Ryan, N. Thomas, and R. Tetrault. 2012. “Complementarity of Resourcesat-1 AWiFS and Landsat TM/ETM+ Sensors.” Remote Sensing of Environment 123: 41–56. doi:10.1016/j.rse.2012.03.002.
   Web of Science ®Google Scholar
• Güneralp, I., A. M. Filippi, and B. U. Hales. 2013. “River-Flow Boundary Delineation from Digital Aerial Photography and Ancillary Images Using Support Vector Machines.” GIScience & Remote Sensing 50 (1): 1–25.
   Web of Science ®Google Scholar
• Hu, J., and S. Li. 2012. “The Multiscale Directional Bilateral Filter and Its Application to Multisensor Image Fusion.” Information Fusion 13: 196–206. doi:10.1016/j.inffus.2011.01.002.
   Web of Science ®Google Scholar
• Ishak, A. M., R. Remesan, P. K. Srivastava, T. Islam, and D. Han. 2013. “Error Correction Modelling of Wind Speed through Hydro-Meteorological Parameters and Mesoscale Model: A Hybrid Approach.” Water Resources Management 27 (1): 1–23. doi:10.1007/s11269-012-0130-1.
   Web of Science ®Google Scholar
• Islam, T., P. K. Srivastava, M. Gupta, X. Zhu, and S. Mukherjee. eds. 2014. Computational Intelligence Techniques in Earth and Environmental Sciences, 281. Dordrecht: Springer.
   Google Scholar
• ISRO. 2012. “Annual Report.” Accessed 13 May, 2014 www.isro.org/pdf/AnnuaReport2012.pdf‎
   Google Scholar
• Jiji, C. V., and S. Chaudhuri. 2006. “Single-Frame Image Super-Resolution through Contourlet Learning.” EURASIP Journal on Applied Signal Processing 2006: 235–235.
   Google Scholar
• Jiji, C. V., M. V. Joshi, and S. Chaudhuri. 2004. “Single‐Frame Image Super‐Resolution Using Learned Wavelet Coefficients.” International Journal of Imaging Systems and Technology 14: 105–112. doi:10.1002/ima.20013.
   Web of Science ®Google Scholar
• Kerr, J. T., and M. Ostrovsky. 2003. “From Space to Species: Ecological Applications for Remote Sensing.” Trends in Ecology and Evolution 18: 299–305. doi:10.1016/S0169-5347(03)00071-5.
   Web of Science ®Google Scholar
• Kim, K. I., D. H. Kim, and J. H. Kim. 2004. “Example-Based Learning for Image Super-Resolution,” In Proc. 3rd Tsinghua-KAIST Joint Workshop Pattern Recognition, Beijing, December 16–17, 140–148.
   Google Scholar
• Kim, K. I., and Y. Kwon. 2010. “Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior.” IEEE Transactions on Pattern Analysis and Machine Intelligence 32: 1127–1133. doi:10.1109/TPAMI.2010.25.
   PubMed Web of Science ®Google Scholar
• Kim, Y. H., J. Im, H. K. Ha, J. K. Choi, and S. Ha. 2014. “Machine Learning Approaches to Coastal Water Quality Monitoring Using GOCI Satellite Data.” GIScience & Remote Sensing 51 (2): 158–174.
   Web of Science ®Google Scholar
• La Rosa, D., and D. Wiesmann. 2013. “Land Cover and Impervious Surface Extraction Using Parametric and Non-Parametric Algorithms from the Open-Source Software R: An Application to Sustainable Urban Planning in Sicily.” GIScience & Remote Sensing 50 (2): 231–250.
   Web of Science ®Google Scholar
• Li, M., J. Im, and C. Beier. 2013. “Machine Learning Approaches for Forest Classification and Change Analysis Using Multi-Temporal Landsat TM Images over Huntington Wildlife Forest.” GIScience & Remote Sensing 50 (4): 361–384.
   Web of Science ®Google Scholar
• Li, S., B. Yang, and J. Hu. 2011. “Performance Comparison of Different Multi-Resolution Transforms for Image Fusion.” Information Fusion 12 (2): 74–84. doi:10.1016/j.inffus.2010.03.002.
   Web of Science ®Google Scholar
• Loew, A., R. Ludwig, and W. Mauser. 2006. “Derivation of Surface Soil Moisture from ENVISAT ASAR Wide Swath and Image Mode Data in Agricultural Areas.” IEEE Transactions on Geoscience and Remote Sensing 44: 889–899. doi:10.1109/TGRS.2005.863858.
   Web of Science ®Google Scholar
• Milanfar, P., ed. 2010. Super-Resolution Imaging. Boca Raton, FL: CRC Press.
   Google Scholar
• Ni, K., and T. Q. Nguyen. 2007. “Image Superresolution Using Support Vector Regression.” IEEE Transactions on Image Processing 16: 1596–1610. doi:10.1109/TIP.2007.896644.
   PubMed Web of Science ®Google Scholar
• Sanyal, J., and X. X. Lu. 2004. “Application of Remote Sensing in Flood Management with Special Reference to Monsoon Asia: A Review.” Natural Hazards 33: 283–301. doi:10.1023/B:NHAZ.0000037035.65105.95.
   Web of Science ®Google Scholar
• Srivastava, P. K., D. Han, M. R. Ramirez, and T. Islam. 2013. “Machine Learning Techniques for Downscaling SMOS Satellite Soil Moisture Using MODIS Land Surface Temperature for Hydrological Application.” Water Resources Management 27 (8): 3127–3144. doi:10.1007/s11269-013-0337-9.
   Web of Science ®Google Scholar
• Srivastava, P. K., D. Han, M. A. Rico-Ramirez, M. Bray, T. Islam, M. Gupta, and Q. Dai. 2014. “Estimation of Land Surface Temperature from Atmospherically Corrected LANDSAT TM Image Using 6S and NCEP Global Reanalysis Product.” Environmental Earth Sciences. doi:10.1007/s12665-014-3388-1.
   Web of Science ®Google Scholar
• Walthall, C. L., J. M. Norman, J. M. Welles, G. Campbell, and B. L. Blad. 1985. “Simple Equation to Approximate the Bidirectional Reflectance from Vegetative Canopies and Bare Soil Surfaces.” Applied Optics 24: 383–387. doi:10.1364/AO.24.000383.
   PubMed Web of Science ®Google Scholar
• Wang, Z., A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. 2004. “Image Quality Assessment: From Error Visibility to Structural Similarity.” IEEE Transactions on Image Processing 13 (4): 600–612. doi:10.1109/TIP.2003.819861.
   PubMed Web of Science ®Google Scholar
• Zhang, H., and B. Huang. 2013. “Support Vector Regression-Based Downscaling for Intercalibration of Multiresolution Satellite Images.” IEEE Transactions on Geoscience and Remote Sensing 51: 1114–1123. doi:10.1109/TGRS.2013.2243736.
   Web of Science ®Google Scholar