Journal of Mountain Science

, Volume 14, Issue 7, pp 1391–1404 | Cite as

Response of fuzzy clustering on different threshold determination algorithms in spectral change vector analysis over Western Himalaya, India

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Abstract

Change detection is a standard tool to extract and analyze the earth’s surface features from remotely sensed data. Among the different change detection techniques, change vector analysis (CVA) have an exceptional advantage of discriminating change in terms of change magnitude and vector direction from multispectral bands. The estimation of precise threshold is one of the most crucial task in CVA to separate the change pixels from unchanged pixels because overall assessment of change detection method is highly dependent on selected threshold value. In recent years, integration of fuzzy clustering and remotely sensed data have become appropriate and realistic choice for change detection applications. The novelty of the proposed model lies within use of fuzzy maximum likelihood classification (FMLC) as fuzzy clustering in CVA. The FMLC based CVA is implemented using diverse threshold determination algorithms such as double-window flexible pace search (DFPS), interactive trial and error (T&E), and 3×3‒pixel kernel window (PKW). Unlike existing CVA techniques, addition of fuzzy clustering in CVA permits each pixel to have multiple class categories and offers ease in threshold determination process. In present work, the comparative analysis has highlighted the performance of FMLC based CVA over improved SCVA both in terms of accuracy assessment and operational complexity. Among all the examined threshold searching algorithms, FMLC based CVA using DFPS algorithm is found to be the most efficient method.

Keywords

Change vector analysis (CVA) Fuzzy maximum likelihood classification (FMLC) Doublewindow flexible pace search (DFPS) Interactive trial and error (T&E) Pixel kernel window (PKW) 

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References

  1. Allen TR, Kupfer JA (2000) Application of spherical statistics to change vector analysis of Landsat data: Southern Appalachian spruce-fir forests. Remote Sensing of Environment 74(3): 482–493. DOI: 10.1016/S0034-4257(00)00140-1CrossRefGoogle Scholar
  2. Baisantry M, Negi DS, Manocha OP (2012) Change vector analysis using enhanced PCA and inverse triangular functionbased thresholding. Defence Science Journal 62(4): 236–242. DOI: 10.14429/dsj.62.1072CrossRefGoogle Scholar
  3. Chen J, Gong, P, He C, et al. (2003) Landuse/land-cover change detection using improved change-vector analysis. Photogrammetric Engineering and Remote Sensing 69(4): 369–379. DOI:10.14358/PERS.69.4.369CrossRefGoogle Scholar
  4. Chen J, Chen X, Cui X et al. (2011) Change vector analysis in posterior probability space: a new technique for land cover change detection. IEEE Geo-science and Remote Sensing Letters 8(2): 317–321. DOI: 10.1109/LGRS.2010.2068537CrossRefGoogle Scholar
  5. He C, Zhao Y, Tian J, et al. (2013) Improving change vector analysis by cross-correlogram spectral matching for accurate detection of land-cover conversion. International Journal of Remote Sensing 34(4): 1127–1145. DOI: 10.1080/01431161.2012.718458CrossRefGoogle Scholar
  6. ERDAS (1999) ERDAS: Field Guide. ERDAS Inc., Atlanta, Georgia, p 671.Google Scholar
  7. Fung T, LeDrew E (1988) The determination of optimal threshold levels for change detection using various accuracy indices. Photogrammetric Engineering & Remote Sensing 54: 1449–1454.Google Scholar
  8. Foody GM, Atkinson PM (2002) Uncertainty in remote sensing and GIS, John Wiley & Sons. DOI: 10.1002/0470035269CrossRefGoogle Scholar
  9. Johnson RD, Kasischke ES (1998) Change vector analysis: a technique for the multitemporal monitoring of land cover and condition. International Journal of Remote Sensing 19: 411–426. DOI: 10.1080/014311698216062CrossRefGoogle Scholar
  10. Kontoes CC (2008) Operational land cover change detection using change vector analysis. International Journal of Remote Sensing 29(16): 4757–4779. DOI: 10.1080/0143116080196 1367CrossRefGoogle Scholar
  11. Lambin EF, Strahler AH (1994) Change-vector analysis in multitemporal space: a tool to detect and categorize land-cover change processes using high temporal-resolution satellite data. Remote Sensing of Environment 48(2): 231–244. DOI: 10.1016/0034-4257(94)90144-9CrossRefGoogle Scholar
  12. Lu D, Mausel P, Brondizio E, et al. (2004) Change detection techniques. International Journal of Remote Sensing 25(12): 2365–2407. DOI: 10.1080/0143116031000139863CrossRefGoogle Scholar
  13. Malila WA (1980) Change vector analysis: an approach for detecting forest changes with Landsat, In Proceedings of the 6th Annual Symposium on Machine Processing of Remotely Sensed Data, 3–6 June 1980, West Lafayette, IN (West Lafayette: Purdue University), 326-335.Google Scholar
  14. Mather PM (2004) Computer processing of remotely-sensed images: an introduction, Wiley, 2, Chichester.Google Scholar
  15. Melesse AM, Jordan JD (2002) A comparison of fuzzy vs. augmented-ISODATA classification algorithms for cloudshadow discrimination from Landsat images. Photogrammetric Engineering & Remote Sensing 68: 905–911. DOI: 35400010918127.0030Google Scholar
  16. Michalek JL, Wagner TW, Luczkovich JJ, et al. (1993) Multispectral change vector analysis for monitoring coastal marine environments. Photogrammetric Engineering and Remote Sensing 59: 381–384.Google Scholar
  17. Mishra VD, Sharma JK, Singh KK, et al. (2009a) Assessment of different topographic corrections in AWiFS satellite imagery of Himalaya terrain. Journal of Earth System Sciences 118(1): 11–26. DOI: 10.1007/s12040-009-0002-0CrossRefGoogle Scholar
  18. Mishra VD, Sharma JK and Khanna R (2009b) Review of topographic analysis techniques for the western Himalaya using AWiFS and MODIS satellite imagery. Annals of Glaciology 51(54): 1–8. DOI: 10.3189/172756410791386526Google Scholar
  19. Mishra NS, Ghosh S, Ghosh A (2012) Fuzzy clustering algorithms incorporating local information for change detection in remotely sensed images. Applied Soft Computing 12: 2683–2692. DOI: 10.1016/j.asoc.2012.03.060CrossRefGoogle Scholar
  20. Nackaerts K, Vaesen K, Muys B, et al. (2005) Comparative performance of a modified change vector analysis in forest change detection. International Journal of Remote Sensing 26(5): 839–852. DOI: 10.1080/0143116032000160462CrossRefGoogle Scholar
  21. Nichol J, Hang LK, Sing WM (2006) Empirical correction of low sun angle images in steeply sloping terrain: a slope matching technique. International Journal of Remote Sensing 27(3-4): 629–635. DOI: 10.1080/02781070500293414CrossRefGoogle Scholar
  22. Sharma JK, Mishra VD, 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. DOI: 10.1007/s12524-011-0180-5CrossRefGoogle Scholar
  23. Silva PG, Santos JR, Shimabukuro YE, et al. (2003) Change vector analysis technique to monitor selective logging activities in Amazon. IEEE Proceedings International Geoscience and Remote Sensing Symposium, 2580–2582. DOI: 10.1109/IGARSS.2003.1294515Google Scholar
  24. Singh S, Sharma JK, Mishra VD (2011) Comparison of different topographic correction methods using AWiFS satellite data. International Journal of Advanced Engineering Sciences and Technologies 7(1): 85–91.Google Scholar
  25. Singh S, and Talwar R (2014) A comparative study on change vector analysis based change detection techniques. SADHANA-Academy Proceedings in Engineering Sciences 39(6): 1311–1331. DOI: 10.1007/s12046-014-0286-xGoogle Scholar
  26. Singh S, Talwar R (2015a) Assessment of different CVA based change detection techniques using MODIS dataset. MAUSAM Journal 66(1): 77–86.Google Scholar
  27. Singh S, Talwar R (2015b) Performance analysis of different threshold determination techniques for change vector analysis. Journal of Geological Society of India 86: 52–58. DOI: 10.1007/s12594-015-0280-xCrossRefGoogle Scholar
  28. Singh S, Talwar R (2016) An intercomparison of different topography effects on discrimination performance of fuzzy change vector analysis algorithm. Meteorology Atmospheric Physics 128(6): 1–14. DOI: 10.1007/s00703-016-0494-5CrossRefGoogle Scholar
  29. Thonfeld F, Hannes F, Braunc M, et al. (2016) Robust Change Vector Analysis (RCVA) for multi-sensor very high resolution optical satellite data. International Journal of Applied Earth Observation and Geoinformation 50: 131–140. DOI: 10.1016/j.jag.2016.03.009CrossRefGoogle Scholar
  30. Varshney A, Arora MK, Ghosh JK (2012) Median change vector analysis algorithm for land-use land-cover change detection from remote-sensing data. Remote Sensing Letters 3(7): 605–614. DOI: 10.1080/01431161.2011.648281CrossRefGoogle Scholar
  31. Zhang J, Foody GM (1998) A fuzzy classification of sub-urban land cover from remotely sensed imagery. International Journal of Remote Sensing 9(14): 2721–2738. DOI: 10.1080/01431169821447CrossRefGoogle Scholar

Copyright information

© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringChitkara UniversityHimachal PradeshIndia
  2. 2.Chandigarh Group of Colleges, Technical CampusJhanjeriIndia

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