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Journal of the Indian Society of Remote Sensing

, Volume 46, Issue 12, pp 1991–2002 | Cite as

Spatial and Quantitative Comparison of Topographically Derived Different Classification Algorithms Using AWiFS Data over Himalayas, India

  • Vishakha Sood
  • Sheifali Gupta
  • Hemendra Singh Gusain
  • Sartajvir Singh
Research Article
  • 21 Downloads

Abstract

In recent years, the significant increase in research on spatial information is observed. Classification or clustering is one of the well-known methods in spatial data analysis. Traditionally, classifiers are generally based on per-pixel approaches and are not utilizing the spatial information within pixel, called mixels which is an important source of information to image classification. There are two foremost reasons behind the existence of mixels: (a) coarse or low spatial resolution of sensor and (b) topographic effects that recorded on optical satellite imagery due to differential terrain illuminations over rugged areas such as Himalayas. In the present study, different classification algorithms have been implemented to drive the impact of topography on them. Among various available, three algorithms for the mapping of snow cover region over north Indian Himalayas (India) are compared: (a) maximum likelihood classification (MLC) as supervised classifier; (b) k-mean clustering as unsupervised classifier; and (c) linear spectral mixing model (LSMM) as soft classifier. These algorithms have been implemented on AWiFS multispectral data, and analysis was carried out. The classification accuracy is estimated by the error matrices, and LSMM achieved higher accuracy (84.5–88.5%) as compared to MLC (81–84%) and k-mean (74–81%). The results highlight that topographically derived classifiers achieved better accuracy in mapping as compared to simple classifiers. The study has many applications in snow hydrology, glaciology and climatology of mountain topography.

Keywords

Topographic correction (TC) Classification algorithms Subpixel classification Himalayas Advance Wide Field Sensor (AWiFS) satellite data 

Notes

Acknowledgements

The authors would like to thank Indian Remote Sensing (IRS) for their great efforts in developing and distributing remotely sensed AWiFS satellite data and their DEM products online to public for free downloading. Thanks are also due to United States Geological Survey (USGS) for providing ASTER Global DEM and Landsat 8 data for research and educational purposes.

Compliance with Ethical Standards

Conflict of interest

No potential conflict of interest was reported by the authors.

References

  1. Besheer, M., & Abdelhafiz, A. (2015). Modified invariant colour model for shadow detection. International Journal of Remote Sensing, 36(24), 6214–6223.  https://doi.org/10.1080/01431161.2015.1112930.CrossRefGoogle Scholar
  2. Celik, T. (2009). Unsupervised change detection in satellite images using principal component analysis and k-means clustering. IEEE Geoscience and Remote Sensing Letters, 6(4), 772–776.  https://doi.org/10.1109/LGRS.2009.2025059.CrossRefGoogle Scholar
  3. Colby, J. D. (1991). Topographic normalization in rugged terrain. Photogrammetry Engineering Remote Sensing, 57(5), 531–537.Google Scholar
  4. Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35–46.  https://doi.org/10.1016/0034-4257(91)90048-b.CrossRefGoogle Scholar
  5. Dare, P. M. (2005). Shadow analysis in high-resolution satellite imagery of urban areas. Photogrammetric Engineering & Remote Sensing, 71(2), 169–177.CrossRefGoogle Scholar
  6. Dean, A. M., & Smith, G. M. (2003). An evaluation of per-parcel land cover mapping using maximum likelihood class probabilities. International Journal of Remote Sensing, 24(14), 2905–2920.  https://doi.org/10.1080/01431160210155910.CrossRefGoogle Scholar
  7. Foody, G. M., & Atkinson, P. M. (Eds.). (2003). Uncertainty in remote sensing and GIS. London: Wiley.  https://doi.org/10.1002/0470035269.CrossRefGoogle Scholar
  8. Gao, M. L., Zhao, W. J., Gong, Z. N., Gong, H. L., Chen, Z., & Tang, X. M. (2014). Topographic correction of ZY-3 satellite images and its effects on estimation of shrub leaf biomass in mountainous areas. Remote Sensing, 6(4), 2745–2764.  https://doi.org/10.3390/rs6042745.CrossRefGoogle Scholar
  9. HongLei, Y., JunHuan, P., BaiRu, X., & DingXuan, Z. (2013). Remote sensing classification using fuzzy C-means clustering with spatial constraints based on Markov random field. European Journal of Remote Sensing, 46(1), 305–316.  https://doi.org/10.5721/eujrs20134617.CrossRefGoogle Scholar
  10. Hudson, W. D., & Ramm, C. W. (1987). Correct formulation of the kappa coefficient of agreement. Photogrammetric Engineering and Remote Sensing, 53(4), 421–422.Google Scholar
  11. Lillesand, T., Kiefer, R. W., & Chipman, J. (2015). Remote sensing and image interpretation (7th ed.). London: Wiley.Google Scholar
  12. Ling, F., Du, Y., Xiao, F., & Li, X. (2012). Subpixel land cover mapping by integrating spectral and spatial information of remotely sensed imagery. IEEE Geoscience and Remote Sensing Letters, 9(3), 408–412.  https://doi.org/10.1109/Lgrs.2011.2169934.CrossRefGoogle Scholar
  13. Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823–870.  https://doi.org/10.1080/01431160600746456.CrossRefGoogle Scholar
  14. Memarsadeghi, N., Mount, D. M., Netanyahu, N. S., & Le Moigne, J. (2007). A fast implementation of the ISODATA clustering algorithm. International Journal of Computational Geometry & Applications, 17(01), 71–103.  https://doi.org/10.1142/s0218195907002252.CrossRefGoogle Scholar
  15. Mishra, V. D., Negi, H. S., Rawat, A. K., Chaturvedi, A., & Singh, R. P. (2009a). Retrieval of sub-pixel snow cover information in the Himalayan region using medium and coarse resolution remote sensing data. International Journal of Remote Sensing, 30(18), 4707–4731.  https://doi.org/10.1080/01431160802651959.CrossRefGoogle Scholar
  16. Mishra, V. D., Sharma, J. K., Singh, K. K., Thakur, N. K., & Kumar, M. (2009b). Assessment of different topographic corrections in AWiFS satellite imagery of Himalaya terrain. Journal of Earth System Science, 118(1), 11–26.  https://doi.org/10.1007/s12040-009-0002-0.CrossRefGoogle Scholar
  17. Nichol, J., Hang, L. K., & Sing, W. M. (2006). Empirical correction of low sun angle images in steeply sloping terrain: A slope matching technique. International Journal of Remote Sensing, 27(3), 629–635.  https://doi.org/10.1080/02781070500293414.CrossRefGoogle Scholar
  18. Nyborg, L., & Sandholt, I. (2001). NOAA-AVHRR based flood monitoring. In IEEE 2001 international geoscience and remote sensing symposium, 2001. IGARSS’01 (Vol. 4, pp. 1696–1698).  https://doi.org/10.1109/igarss.2001.977041.
  19. Otukei, J. R., & Blaschke, T. (2010). Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, 12, S27–S31.  https://doi.org/10.1016/j.jag.2009.11.002.CrossRefGoogle Scholar
  20. Sharma, V., Mishra, V. D., & Joshi, P. K. (2014). Topographic controls on spatio-temporal snow cover distribution in Northwest Himalaya. International Journal of Remote Sensing, 35(9), 3036–3056.  https://doi.org/10.1080/01431161.2014.894665.CrossRefGoogle Scholar
  21. Sharma, J. K., Mishra, V. D., & Khanna, R. (2013). Impact of topography on accuracy of land cover spectral change vector analysis using AWiFS in Western Himalaya. Journal of the Indian Society of Remote Sensing, 41(2), 223–235.  https://doi.org/10.1007/s12524-011-0180-5.CrossRefGoogle Scholar
  22. Simhachalam, B., & Ganesan, G. (2015). Performance comparison of fuzzy and non-fuzzy classification methods. Egyptian Informatics Journal, 17(2), 183–188.  https://doi.org/10.1016/j.eij.2015.10.004.CrossRefGoogle Scholar
  23. Singh, S., Sharma, J. K., & Mishra, V. D. (2011). Topographic influence on improved change vector analysis using MODIS satellite data of western Himalaya. International Journal of Advanced Engineering Sciences and Technologies, 7(1), 77–84.Google Scholar
  24. Singh, S., & Talwar, R. (2017). Response of fuzzy clustering on different threshold determination algorithms in spectral change vector analysis over Western Himalaya, India. Journal of Mountain Science, 14(7), 1391–1404.  https://doi.org/10.1007/s11629-016-4248-0.CrossRefGoogle Scholar
  25. Singh, S., & Talwar, R. (2018). An intercomparison of different topography effects on discrimination performance of fuzzy change vector analysis algorithm. Meteorology and Atmospheric Physics, 130(1), 125–136.  https://doi.org/10.1007/s00703-016-0494-5.CrossRefGoogle Scholar
  26. Sood, V., & Singh, S. (2018). Analytical analysis of different shadow removing algorithms over land-use 1 and land-cover classification. Himalayan Geology, 39(2), 223–232.Google Scholar
  27. Wang, Z., Wei, W., Zhao, S., & Chen, X. (2004). Object-oriented classification and application in land use classification using SPOT-5 PAN imagery. In Geoscience and remote sensing symposium, 2004. IGARSS’04 (Vol. 5, pp. 3158–3160).  https://doi.org/10.1109/igarss.2004.1370370.
  28. Wu, K., Zhong, Y., Wang, X., & Sun, W. (2017). A novel approach to subpixel land-cover change detection based on a supervised back-propagation neural network for remotely sensed images with different resolutions. IEEE Geoscience and Remote Sensing Letters, 14(10), 1750–1754.  https://doi.org/10.1109/LGRS.2017.2733558.CrossRefGoogle Scholar

Copyright information

© Indian Society of Remote Sensing 2018

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringChitkara University Institute of Engineering and Technology, Chitkara University, PunjabPatialaIndia
  2. 2.Snow and Avalanche Study EstablishmentDRDOChandigarhIndia
  3. 3.Department of Electronics and Communication EngineeringChitkara University School of Engineering and Technology, Chitkara University, Himachal PradeshSolanIndia

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