36th COSPAR Scientific Assembly 2006 Beijing July 20, 2006 Hypothesis-Test-Based Landcover Change Detection Using Multitemporal Satellite Images S. P

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.


Major Methods of Change Detection

Post-classification methodsImage-differencing methodsPrincipal component analysis methods


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.


Problems and challenges How should the threshold value be

determined?How much confidence do we have on decision

of change detection?


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


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).


Bivariate Scatter Plot of Multi-temporal Images

Red band

01/10/1999 vs 21/09/2002






Change Detection Using BivariateProbability Contours

95% probability contour X2

90% probability contour

X1

: detected changes


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.


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)


Class-dependent Temporal Correlation

X2

X1


Statistical Aspects of Change Detection

Uncertainties involvedA statistical test requires

The null and alternative hypotheses A test statistic Level of significance


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?


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.


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


Comparison of Confidence Intervals Established by Bivariate Joint Distribution and Conditional Distribution

)()|( 111

22112

xxXXE

22112 1)|( xXXVar


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


Area-A

Pre-period SPOT image21/07/’04

Post-period SPOT image 09/07/’05


Deji Watershed

Deji Dam

Jhongheng Road

SPOT image

Taiwan

Study area

10 0 10 20 Kilometers

Area-B


Pre-period SPOT image (26/06/’04)

Post-period SPOT image (12/07/’04)

Area-B


Confusion matrix – pre-period image (Area-B)

Confusion matrix – post-period image (Area-B)


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?


Grey Level Histogram

IR R G

water

vegetation

soil


IR R G

water

vegetation

soil


Finding the Test Statistic (1/7)

Single-feature change detection



Multiple-feature change detection





It does not yield a conditional distribution for change detection.




Finding the Test Statistic (6/7)Multiple-feature BVN change detection



Bivariate Normal Distribution

cXX )()'( 1 Σ

)(

)(,

1

1

1

1

PCE

PCE

PC

PCX

= Covariance matrix

for any constant c


Area-A

1PC 1PC


(PC1, PC1) BVN Vegetation


(PC1, PC1) BVN Water


(PC1, PC1) BVN Bare Land


Detected ChangesArea-A

α=1% α=5% α=10%




Area-B


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.


Area-A


Area-B


Overall Accuracy vs Level of Significance

88

90

92

94

96

98

100

0 2 4 6 8 10 12

Area-A

Area-B


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.


Thanks for your attention.

Documents

36th COSPAR Scientific Assembly 2006 Beijing July 20, 2006 Hypothesis-Test-Based Landcover Change Detection Using Multitemporal Satellite Images S. P