Spatial Enhancement of AWiFS along Wider Swath using NSCT

  • Authors

    • K S. R. Radhika
    • C V. Rao
    • V Kamakshi Prasad
    2018-07-20
    https://doi.org/10.14419/ijet.v7i3.12.16162
  • Non Sub sampled Contourlet Transform, NSCT training and learning, Spatial Resolution, Temporal Resolution, LISS-III-AWiFS pair (One Pair), Single Image Super Resolution.
  • Image acquisition in a wider swath, cannot assess the best spatial resolution (SR) and temporal resolution (TR) simultaneously, due to inherent limitations of space borne sensors. But any of the information extraction from remote sensed (RS) images demands the above characteristics. As this is not possible onboard, suitable ground processing techniques need to be evolved to realise the requirements through advanced image processing techniques. The proposed work deals with processing of two onboard sensor data viz., Resourcesat-1 (RS1): LISS-III, which has medium swath combined with AWiFS, which has wider swath data to provide high spatial and temporal resolution at the same instant. LISS-III at 23m and 24 days, AWiFS at 56m and 5 days spatial and temporal revisits acquire the data at different swaths. In the process of acquisition at the same time, the 140km swath of LISS-III coincides at the exact centre line 740km swath of AWiFS. If the non-overlapping area of AWiFS has same features of earth’s surface as of LISS-III overlapping area, it then provides a way to increase the SR of AWiFS to SR of LISS-III in the same non-overlapping area. Using this knowledge, a novel processing technique Fast One Pair Learning and Prediction (FOPLP) is developed in which time is optimized against the existing methods. FOPLP improves the SR of LISS-III in non-overlapping area using technique Single Image Super Resolution (SISR) with Non Sub sampled Contourlet Transforms (NSCT) method and is applied on different sets of images. The proposed technique resulting into an image having TR of 5 days, 740km swath at SR of 23m. Results have shown the strength of the proposed method in terms of computation time and prediction accuracy assessment.

     

     

  • References

    1. [1] ASAR Wide Swath and Image Mode Data in Agricultural Areas.†IEEE Transactions on Geosciences and Remote Sensing 44: 889–899. doi:10.1109/TGRS.2005.863858.

      [2] 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.

      [3] 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.

      [4] Chang, H., D. Y. Yeung, and Y. Xiong. 2004. “Super-Resolution through Neighbor Embedding.†Proceedings IEEE Conference CVPR 1: I-275–I-282.

      [5] 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.

      [6] 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.

      [7] 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.

      [8] Auernhammer, H. 2001. “Precision Farming—The Environmental Challenge.†Computers and Electronics in Agriculture 30: 31–43. doi:10.1016/S0168-1699(00)00153-8.

      [9] 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.

      [10] 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.

      [11] 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.

      [12] Jiji, C. V., and S. Chaudhuri. 2006. “Single-Frame Image Super-Resolution through Contourlet Learning.†EURASIP Journal on Applied Signal Processing 2006: 235–235.

      [13] Jiji, C. V., M. V. Joshi, and S. Chaudhuri. 2004. “Singleâ€Frame Image Superâ€Resolution Using Learned Wavelet Coefficients.†International Journal of Imaging Systems and Technology 14: 105–112. doi:10.1002/ima.20013.

      [14] Li, S., B. Yang, and J. Hu. 2011. “Performance Comparison of Different Multi-Resolution Transforms for Image Fusion.†Information F[1] Loew, A., R. Ludwig, and W. Mauser. 2006. “Derivation of Surface Soil Moisture from ENVISAT usion 12 (2): 74–84. doi:10.1016/j. inffus.2010.03.002.

      [15] 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.

      [16] 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.

      [17] 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.

      [18] 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.

      [19] Da Cunha, A. L., J. Zhou, and M. N. Do. 2006. “The Non Subsampled Contourlet Transform: Theory, Design, and Applications.†IEEE Transactions on Image Processing 15: 3089–3101. doi:10.1109/TIP.2006.877507.

      [20] Milanfar, P., ed. 2010. Super-Resolution Imaging. Boca Raton, FL: CRC Press.

      [21] 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.

      [22] 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.

      [23] Kim,Y.H.,J.Im,H.K.Ha,J.K.Choi,andS.Ha.2014. “Machine Learning Approaches to Coastal Water Quality Monitoring Using GOCI Satellite Data.†GIScience & Remote Sensing 51 (2): 158–174.

      [24] 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.

      [25] 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.

      [26] 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.

      [27] 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.

      [28] 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.

      [29] Rao, C.V., MalleswaraRao, J., Senthil Kumar, A., Lakshmi, B., Dadhwal, V.K.: Expansion of LISS-III swath using AWiFS wider swath data and contourlet coffecients learning. In: GI Science& Remote Sensing, 52(1); 78-93, doi:10.1080/15481603.2014, 983370 (2015).

      [30] Radhika, K.S.R., Rao, C.V., Kamakshi Prasad, V.: Enhancement of AWiFS Spatial Resolution with SVM Learning. In 6th International Advanced Computing Conference, 978-1-4673-8286-1/16 IEEE DOI 10.1109/IACC.2016.42, pp. 178-183 (2016).

      [31] National Remote Sensing Centre, https://bhuvan.nrsc.gov.in.

      [32] Bamberger, R.H., Smith, M.J.T.: A Filter Bank for the Directional Decomposition of Images: Theory and design. In: IEEE Trans. Signal Process., vol. 40, no. 4, pp. 882-893, Ar. (1992).

      [33] Xun Liu, Chenwei Deng, Baojun Zhao.: Spatiotemporal Reflectance Fusion Based on Location Regularized Sparse Representation. In: IGARSS, 978-1-5090-3332-4/16/$31.00 ©2016 IEEE, pp. 2562-2565 (2016).

      [34] A Murali, K. Hari Kishore, “Efficient and High Speed Key Independent AES Based Authenticated Encryption Architecture using FPGAs “International Journal of Engineering and Technology(UAE), ISSN No: 2227-524X, Vol No: 7, Issue No: 1.5, Page No: 230-233, January 2018

  • Downloads

  • How to Cite

    S. R. Radhika, K., V. Rao, C., & Kamakshi Prasad, V. (2018). Spatial Enhancement of AWiFS along Wider Swath using NSCT. International Journal of Engineering & Technology, 7(3.12), 474-480. https://doi.org/10.14419/ijet.v7i3.12.16162