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Hypothesis-Test-Based Landcover Change Detection Using Multitemporal Satellite Images

Hypothesis-Test-Based Landcover Change Detection Using Multitemporal Satellite Images. S. P. Teng, J. L. Chiang, Y. K. Chen, K. S. Cheng Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering National Taiwan University. Change Detection.

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Hypothesis-Test-Based Landcover Change Detection Using Multitemporal Satellite Images

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  1. Hypothesis-Test-Based Landcover Change Detection Using Multitemporal Satellite Images S. P. Teng, J. L. Chiang, Y. K. Chen, K. S. Cheng Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering National Taiwan University 36th COSPAR Scientific Assembly 2006 Beijing July 20, 2006

  2. Change Detection • Change detection is the process of identifying differences in the state of an object or phenomenon by observing it at different times. • In general, change detection involves the application of multi-temporal datasets to quantitatively analyze the temporal effects of the phenomenon. • Because of the advantages of repetitive data acquisition, its synoptic view, and digital format suitable for computer processing, remotely sensed data have become the major data sources for different change detection applications during the past decades. 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  3. Major Methods of Change Detection • Post-classification methods • Image-differencing methods • Principal component analysis methods 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  4. Image Differencing Method for Change Detection • The process basically calculates grey level difference between two images and adopts a threshold value of grey level difference for land-cover change detection. • Image differencing on single band or composite images is the most widely used approach of change detection. 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  5. Problems and challenges • How should the threshold value be determined? • How much confidence do we have on decision of change detection? 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  6. Determining Threshold for Change Detection Multiples of standard deviation of DN difference Nelson (1983): k = 0.5~1 Ridd and Liu (1998): k = 0.9~1.4 Sohl (1999): k = 2 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  7. It uses grey level differences of all pixels (including changed and no-change pixels) to determine the threshold. • It does not consider the grey level correlation of multi-temporal images. • Generally speaking, pre- and post-period images of the same spectral band are highly correlated (since changes are rare). 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  8. Bivariate Scatter Plot of Multi-temporal Images Red band 01/10/1999 vs 21/09/2002 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  9. 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  10. 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  11. 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  12. 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  13. Change Detection Using BivariateProbability Contours 95% probability contour X2 90% probability contour X1 : detected changes 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  14. Disadvantage of Using the Bivariate Probability Confidence Interval • Thresholding of the grey level difference is globally based, i.e., all no-change pixels are considered. It fails to consider the effect of the pre-period grey level on the grey level of the post-period. • It is important to examine the conditional probability distribution on the bivariate scatter plot of multi-temporal images. 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  15. Conditional Prob. Distribution Joint Prob. Distribution Bivariate Joint Probability Distribution and Conditional Probability Distribution (post-period DN) X2 X1 (pre-period DN) 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  16. Class-dependent Temporal Correlation X2 X1 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  17. Statistical Aspects of Change Detection • Uncertainties involved • A statistical test requires • The null and alternative hypotheses • A test statistic • Level of significance 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  18. Transforming Change Detection to Hypothesis Test • Using conditional probability distribution the work of change detection can be placed in the framework of a hypothesis test. • Null hypothesis Ho: no change (Therefore, no-change pixels of individual classes are needed.) • Test statistic? 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  19. Bivariate Normal Distribution • Conditional normal distribution Parameters can be estimated using pixels associated with no change. Critical regions with respect to the chosen level of significance  can then be determined. 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  20. Steps to Establish CI Using Conditional PDF • Identifying no-change pixels • LULC classification for major features (soil, vegetation and water) for pre- and post-period images respectively. • Class-specific correlation analysis using only no-change pixel pairs • Determining bivariate probability distribution for each class • Determining the class-specific conditional PDF • Specifying class-specific critical regions for test at level of significance  36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  21. Comparison of Confidence Intervals Established by Bivariate Joint Distribution and Conditional Distribution 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  22. Study Area and Data • The Chi-Jia-Wan Creek watershed (Area-A, 71 km2) and the an area (Area-B, 250 km2) down-stream of the Te-Chi Reservoir in central Taiwan. • Multispectral SPOT-5 images and 1/5000 airphotos were used. Area-A Area-B 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  23. Pre-period SPOT image 21/07/’04 Area-A Post-period SPOT image 09/07/’05 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  24. Area-B 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  25. Pre-period SPOT image (26/06/’04) Area-B Post-period SPOT image (12/07/’04) 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  26. Confusion matrix – pre-period image (Area-B) Confusion matrix – post-period image (Area-B) 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  27. Establishing the Bivariate Joint Distribution Change detection using single-feature images. versus Change detection using multiple-feature images (e.g., NDVI, principal components, etc.). Does normality hold for the selected feature? 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  28. Grey Level Histogram IR R G water vegetation soil 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  29. IR R G water vegetation soil 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  30. Finding the Test Statistic (1/7) • Single-feature change detection 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  31. Finding the Test Statistic (2/7) • Multiple-feature change detection 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  32. Finding the Test Statistic (3/7) 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  33. Finding the Test Statistic (4/7) It does not yield a conditional distribution for change detection. 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  34. Finding the Test Statistic (5/7) 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  35. Finding the Test Statistic (6/7) • Multiple-feature BVN change detection 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  36. Finding the Test Statistic (7/7) Bivariate Normal Distribution for any constant c = Covariance matrix 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  37. Area-A 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  38. (PC1, PC1) BVN Vegetation 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  39. (PC1, PC1) BVN Water 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  40. (PC1, PC1) BVN Bare Land 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  41. Detected ChangesArea-A α=1% α=10% α=5% 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  42. 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  43. 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  44. Area-B 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  45. Validation • Through field investigation and using high resolution airphotos a set of validation data including both changed and no-change pixels were carefully selected. • Confusion matrices were established for performance evaluation of the proposed approach. 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  46. Area-A 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  47. Area-B 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  48. Overall Accuracy vs Level of Significance Area-A Area-B 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  49. Summary • We have demonstrated that change detection can be placed in a hypothesis test framework. • By using the conditional distribution high accuracy of change detection can be achieved. 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

  50. Thanks for your attention. 36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University

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