A novel method based on Change Vector Analysis to improve results of Change Detection Process

Document Type : علمی - پژوهشی

Authors

1 MSc. Student of Remote Sensing, Department of Civil, Faculty of Engineering, Ferdowsi University of Mashhad

2 MSc. Student of Remote Sensing, Department of Remote Sensing and GIS, Faculty of Geography, Kharazmi University, Tehran

3 Assistant Prof., Department of Civil, Faculty of Engineering, Ferdowsi University of Mashhad

Abstract

The change detection using satellite images is one of the main fields of remote sensing researches. Numerous errors in the images are one problem with the use of satellite imagery. Errors caused by surface illumination, are the main problems in the process of change detection. Therefore, in this study, in order to reduce errors in the results of the detection of changes using change vector analysis, a simple, yet effective method to reduce errors caused by topography is proposed. After applying Tasseled Cap Transform on images, the proposed method compare the angle between direction of change vector with the direction of vector of the pixel in the base image using spectral space, and then calculates the angular threshold. The area under the ROC curve, the Probability Detection and False alarm of proposed method are 0.970, 0.97 and 0.32 respectively.

Keywords

References

  1. Abdi, H. & Williams, L.J., 2010, Principal Component Analysis, Wiley Interdisciplinary Reviews: Computational Statistics, 2(4), 433-459.
  2. Adams, J.B. & Gillespie, A.R., 2006, Remote Sensing of Landscapes with Spectral Images: A Physical Modeling Approach, Cambridge University Press.
  3. Adar, S., Shkolnisky, Y. & Ben Dor, E., 2014, A New Approach for Thresholding Spectral Change Detection Using Multispectral and Hyperspectral Image Aata, a Case Study Over Sokolov, Czech Republic, International Journal of Remote Sensing, 35(4), 1563-1584.
  4. Allen, T.R., 2000, Topographic Normalization of Landsat Thematic Mapper Data in Three Mountain Environments, Geocarto International, 15(2), 15-22.
  5. Ben‐Dor, E., Schläpfer, D., Plaza, A.J., & Malthus, T., 2013, Hyperspectral Remote Sensing, Airborne Measurements for Environmental Research: Methods and Instruments, 413-456.
  6. Congalton, R.G. & Green, K., 2008, Assessing the Accuracy of Remotely Sensed Data: Principles and Practice, CRC press.
  7. 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.
  8. Huang, C., Wylie, B., Yang, L., Homer, C. & Zylstra, G., 2002, Derivation of a Tasselled Cap Transformation Based on Landsat 7 At-Satellite Reflectance, International Journal of Remote Sensing, 23(8), 1741-1748.
  9. Huang, W., Zhang, L., Furumi, S., Muramatsu, K., Daigo, M. & Li, P., 2010, Topographic Effects on Estimating Net Primary Productivity of Green Coniferous Forest in Complex Terrain Using Landsat Data: A Case Study of Yoshino Mountain, Japan, International Journal of Remote Sensing, 31(11), 2941-2957.
  10. Hussain, M., Chen, D., Cheng, A., Wei, H. & Stanley, D., 2013, Change Detection from Remotely Sensed Images: From Pixel-Based to Object-Based Approaches, ISPRS Journal of Photogrammetry and Remote Sensing, 80, 91-106.
  11. Jensen, J.R., 1996, Introductory Digital Image Processing: A Remote Sensing Perspective (No. Ed. 2). Prentice-Hall Inc.
  12. Kauth, R.J. & Thomas, G.S., 1976, The Tasselled Cap--A Graphic Description of the Spectral-Temporal Development of Agricultural Crops as Seen by Landsat, In LARS Symposia (p. 159).
  13. Kresse, W. & Danko, D.M., 2012, Springer Handbook of Geographic Information, Springer.
  14. Malila, W.A., 1980, Change Vector Analysis: An Approach for Detecting Forest Changes with Landsat, In LARS Symposia (p. 385).
  15. Mena, J.B. & Malpica, J.A., 2006, A Change Detection Method with Radiometric Normalization and Shadows Removal in Multispectral Satellite Imagery.
  16. Otsu, N., 1975, A Threshold Selection Method from Gray-Level Histograms, Automatica, 11(285-296), 23-27.
  17. Richter, R., Kellenberger, T. & Kaufmann, H., 2009, Comparison of Topographic Correction Methods, Remote Sensing, 1(3), 184-196.
  18. Sezgin, M., 2004, Survey Over Image Thresholding Techniques and Quantitative Performance Evaluation, Journal of Electronic Imaging, 13(1), 146-168.
  19. Singh, A., 1989, Review Article Digital Change Detection Techniques Using Remotely-Sensed Data, International Journal of Remote Sensing, 10(6), 989-1003.
  20. Ünsalan, C., 2012, Two-Dimensional Change Detection Methods: Remote Sensing Applications, Springer Science & Business Media.
  21. Yang, X. & Lo, C.P., 2000, Relative Radiometric Normalization Performance for Change Detection from Multi-Date Satellite Images, Photogrammetric Engineering and Remote Sensing, 66(8), 967
Iranian Journal of Remote Sensing & GIS
Volume 7, Issue 3 - Serial Number 3
November 2015
Pages 83-96

Files

Share

How to cite

Statistics