Hybrid methods for automatic landscape change detection in noisy data environment
A.A. Afanasyev, A.V. Zamyatin

 

National Research Tomsk State University, Tomsk, Russia

Full text of article: Russian language.

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Abstract:
We consider most widely used practical methods for land cover change detection based on remote data sensing. Based on these methods, approaches to constructing hybrid methods are proposed. Results of the experimental study of the proposed methods in the presence of noise of various types and intensities are discussed. Based on the results of the experiments, hybrid methods that allow one to achieve a better quality in automatic change detection when compared to the known methods are determined.

Keywords:
landscape cover, change detection, landscape dynamics, change detection hybrid methods, digital image processing, image analysis, remote sensing and sensors, detection.

Citation:
Afanasyev AA, Zamyatin AV. Hybrid methods for automatic landscape change detection in noisy data environment. Computer Optics 2017; 41(3): 431-440. DOI: 10.18287/2412-6179-2017-41-3-431-440.

References:

  1. Khandelwal P, Singh KK, Singh BK, Mehrotra A. Unsupervised change detection of multispectral images using wavelet fusion and kohonen clustering network. International Journal of Engineering and Technology 2013; 5(2): 1401-1406.
  2. Lu D, Mausel P. Change detection techniques. International Journal of Remote Sensing 2004; 25(12): 2365-2407. DOI: 10.1080/0143116031000139863.
  3. Hussain M, Chen D, Cheng A, Wei H, Stanley D. Change detection from remotely sensed images: from pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing 2013; 80: 91-106. DOI: 10.1016/j.isprsjprs.2013.03.006.
  4. Lu D, Li G, Moran E. Current situation and needs of change detection techniques. International Journal of Image and Data Fusion 2014; 5(1): 13-38. DOI: 10.1080/19479832.2013.868372.
  5. Collins JB., Woodcock CE. An assessment of several linear change detection techniques for mapping forest mortality using multitemporal Landsat TM data. Remote sensing of environment 1996; 56(1): 66-77. DOI: 10.1016/0034-4257(95)00233-2.
  6. Fedoseev V, Chupshev N. Research of methods for man-made change detection on earth surface using high resolution satellite image series [In Russian]. Computer Optics 2012; 36(2): 279-288.
  7. Ridd MK, Liu JA. Comparison of four algorithms for change detection in an urban environment. Remote Sens Environ 1998; 63(2): 95-100. DOI: 10.1016/S0034-4257(97)00112-0.
  8. Singh A. Digital change detection techniques using remotely sensed data. International Journal of Remote Sensing 1989; 10(6): 989-1003. DOI: 10.1080/01431168908903939.
  9. Vasil’ev KK, Krasheninnikov VR, Tashlinskii AG. Statistical analysis of multidimensional image sequences [In Russian]. Naukoemkie tekhnologii 2013; 5: 5-11.
  10. Jianya G., Haigang S., Guorui M., Qiming Z. A review of multitemporal remote sensing data change detection algorithms. The International Archives of the Photogrammetry. Remote Sensing and Spatial Information Sciences 2008; 37(B7): 757-762.
  11. Dhakal AS, Amada T, Aniya M, Sharma RR. Detection of areas associated with flood and erosion caused by a heavy rainfall using multitemporal Landsat TM data. Photogrammetric Engineering and Remote Sensing 2002; 68(3): 233-240.
  12. Yuan D, Elvidge C. NALC land cover change detection pilot study Washington D.C. area experiments // Remote Sensing of Environment 1998; 66(2): 166-178. DOI: 10.1016/S0034-4257(98)00068-6.
  13. Muchoney DM, Haack BN. Change detection for monitoring forest defoliation. Photogrammetric Engineering and Remote Sensing 1994; 60(10): 1243-1251.
  14. Macleod RD, Сongalton RG. A quantitative comparison of change detection algorithms for monitoring eelgrass from remotely sensed data. Photogrammetric Engineering and Remote Sensing 1998; 64(3): 207-216.
  15. Mas J-F. Monitoring land-cover changes: a comparison of change detection techniques. International Journal of Remote Sensing 1999; 20(1): 139-152. DOI: 10.1080/014311699213659
  16. Gong P. Change detection using principal component analysis and fuzzy set theory. Canadian Journal of Remote Sensing 1993; 19(1): 22-29. DOI: 10.1080/07038992.1993.10855147
  17. Du P, Liu S, Gamba P, Tan K, Xia J. Fusion of difference images for change detection over urban areas. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2012; 5(4): 1076-1086. DOI: 10.1109/JSTARS.2012.2200879.
  18. Almutairi A, Warner TA. Change detection accuracy and image properties: a study using simulated data. Remote Sensing 2010; 2(6): 1508-1529. DOI: 10.3390/rs2061508.
  19. Afanasyev AA, Zamyatin AV. The applicability analysis of the approaches to the identification of land cover changes by remote sensing data [In Russian]. Informational Technologies 2014; 4: 38-42.
  20. Coppin PR, Bauer ME. Processing of multitemporal Landsat TM imagery to optimize extraction of forest cover change features. IEEE Transactions on Geoscience and Remote Sensing 1994; 32(4): 918-927. DOI: 10.1109/36.298020.
  21. Kauth RJ, Thomas GS. The Tasselled Cap - A Graphic Description of the Spectral-Temporal Development of Agricultural Crops as Seen by LANDSAT. LARS Symposia 1976; 41-51.
  22. Niemeyer I, Nussbaum S. Change detection: The potential for nuclear safeguards. In Book: Avenhaus R, Kyriakopoulos N, Richard M, Stein G, eds. Verifying Treaty Compliance. Berlin, Heidelberg: Springer; 2006: 335-348.
  23. McDermid G., Linke J., Pape A.D., Laskin D.N., McLane A.J., Franklin S.E. Object-based approaches to change analysis and thematic map update: challenges and limitations. Canadian Journal of Remote Sensing 2008; 34(5): 462-466. DOI: 10.5589/m08-061.
  24. Al-Khudhairy DHA, Caravaggi I, Giada S. Structural damage assessments from Ikonos data using change detection, object-oriented segmentation, and classification techniques. Photogrammetric Engineering & Remote Sensing 2005; 71(7): 825-837. DOI: 10.14358/PERS.71.7.825.
  25. Gienko AY, Gienko GA, Govorov MO. Geo-information and change detection techniques for ecological assessment of natural resources [In Russian]. Interexpo Geo-Siberia. 2012; 5: 5-11.
  26. Schowengerdt RA. Remote sensing: Models and methods for image processing. 3rd Edition. Orlando, FL: Academic Press, Inc.; 2006. ISBN: 978-0123694072.
  27. Radke RJ. Image change detection algorithms: a systematic survey. IEEE Trans Image Process 2005; 14(3): 294-307. DOI: 10.1109/TIP.2004.838698.
  28. Dianat R, Kasaei S. On automatic threshold selection in regression method for change detection in remote sensing images. Proceedings of the 4th International Symposium on Telecommunications 2008; 1-6.
  29. Horne E, Yanni MK. New approach to dynamic thresholding. EUSIPCO-94: European Conference on Signal Processing, Edinburg 1994; 1: 34-44.
  30. Otsu N. A Threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics 1979; 9(1): 62-63. DOI: 10.1109/TSMC.1979.4310076.
  31. Kittler J, Illingworth J. Minimum error thresholding. Pattern Recognition 1986; 19(1): 41-47. DOI: 10.1016/0031-3203(86)90030-0.
  32. Richards JA, Xiuping J. Remote sensing digital image analysis: An introduction. Berlin: Springer; 1999. ISBN: 978-3642300615.
  33. Ilsever M, Ünsalan C. Two-dimensional change detection methods. London: Springer; 2012. ISBN: 978-1-4471-4254-6.
  34. Pacifici F. Change detection algorithms: State of the art. Source áhttp://www.disp.uniroma2.it/earth_observati­on/pdf/CD-Algorithms.pdfñ.
  35. Sohl T, Terry L. Change analysis in the United Arab Emirates: an investigation of techniques. Photogrammetric Engineering and Remote Sensing 1999; 65(4): 475-484.
  36. Lambin EF., Strahlers AH. 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 1994; 48(2): 231-244. DOI: 10.1016/0034-4257(94)90144-9.
  37. Shlens J. A tutorial on principal component analysis Source: áhttp://www.cs.uu.nl/docs/vakken/ddm/texts/Nor­mal/pca.pdf.ñ
  38. Benlin X, Fangfang L, Xingliang M, Huazhong J. Study on independent component analysis’ application in classification and change detection of multispectral images. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2008; 37(B): 871-875.
  39. Marchesi S., Bruzzone L. ICA and kernel ICA for change detection in multispectral remote sensing images // Geoscience and Remote Sensing Symposium 2009; 2: 980-983. DOI: 10.1109/IGARSS.2009.5418265
  40. Hyvärinen A. Fast and robust fixed-point algorithms for independent component analysis // IEEE Transactions on Neural Networks 1999; 10(3): 626-634. DOI: 10.1109/72.761722.
  41. Hyvärinen A., Oja E. Independent component analysis: algorithms and applications. Neural networks 2000; 13(4): 411-430. DOI: 10.1016/S0893-6080(00)00026-5.
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