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36th COSPAR Scientific Assembly 2006 Beijing July 20, 2006
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 ModelingDept. of Bioenvironmental Systems Engineering
National Taiwan University
36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University
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
Major Methods of Change Detection
Post-classification methodsImage-differencing methodsPrincipal component analysis methods
36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University
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
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
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
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
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
36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University
36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University
36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University
36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University
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
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
Conditional Prob. Distribution
Bivariate Joint Probability Distribution and Conditional Probability Distribution
X2
X1
Joint Prob. Distribution
)|( 12| 112xXf xXX
(pre-period DN)
(post-period DN)
36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University
Class-dependent Temporal Correlation
X2
X1
36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University
Statistical Aspects of Change Detection
Uncertainties involvedA 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
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
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
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
Comparison of Confidence Intervals Established by Bivariate Joint Distribution and Conditional Distribution
)()|( 111
22112
xxXXE
22112 1)|( xXXVar
36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University
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
Area-A
Pre-period SPOT image21/07/’04
Post-period SPOT image 09/07/’05
36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University
Deji Watershed
Deji Dam
Jhongheng Road
SPOT image
Taiwan
Study area
10 0 10 20 Kilometers
Area-B
36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University
Pre-period SPOT image (26/06/’04)
Post-period SPOT image (12/07/’04)
Area-B
36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University
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
Establishing the Bivariate Joint Distribution
Change detection using single-feature images.
versusChange 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
Grey Level Histogram
IR R G
water
vegetation
soil
36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University
IR R G
water
vegetation
soil
36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University
Finding the Test Statistic (1/7)
Single-feature change detection
36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University
Finding the Test Statistic (2/7)
Multiple-feature change detection
36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University
Finding the Test Statistic (3/7)
36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University
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
Finding the Test Statistic (5/7)
36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University
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
Finding the Test Statistic (7/7)
Bivariate Normal Distribution
cXX )()'( 1 Σ
)(
)(,
1
1
1
1
PCE
PCE
PC
PCX
= Covariance matrix
for any constant c
36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University
Area-A
1PC 1PC
36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University
(PC1, PC1) BVN Vegetation
36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University
(PC1, PC1) BVN Water
36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University
(PC1, PC1) BVN Bare Land
36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University
Detected ChangesArea-A
α=1% α=5% α=10%
36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University
36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University
36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University
Area-B
36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University
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
Area-A
36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University
Area-B
36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University
Overall Accuracy vs Level of Significance
88
90
92
94
96
98
100
0 2 4 6 8 10 12
Area-A
Area-B
36th COSPAR Scientific Assembly Beijing, 2006 Dept. of Bioenv. Syst. Eng., National Taiwan University
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
Thanks for your attention.
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