Large area change detection by differencing radiometrically-normalized images
Images Rectification
The year 2000 image was rectified to the UTM map plane by a second degree polynomial transformation
using ground coordinates obtained from GPS’s precise single-point-positioning (PSPP) methods. The
use of GPS PSPP, which can provide coordinate accuracy at around 5 m, to create GCPs (Ground
Control Point) for the rectification process was necessary. This is because the most accurate maps
covering the study areas are topographic maps at the scale of 1:50,000. Cartographic generalization
process such as simplification, exaggeration, and displacement of symbol which are used for the
purpose of enhancing the readability have caused positional error which is undesired side effect if the
maps are to be used to obtain precise coordinates. Combining errors from map generalization with
pointing error and others such as paper shrinking would cause a positional error of more than pixel size
and therefore map-derived GCPs were not suitable. Further, most of the 1:50,000 topographic maps in
these areas have not been updated for almost 20 years, so many GCPs selected on the Landsat image
cannot be located on maps making a GPS campaign to establish GCPs an obvious choice. A total of 17
GCPs were measured by GPS and the RMS error of 0.21 pixel was achieved.
The 1988 image was then co-registered to the 2000 image by second degree polynomial transformation
using 30 common points. The RMS error of the transformation was 0.20 pixel. The good metric accuracy
achieved on both images reassures that ground-truth data located in the field by hand-held GPS, which
provides comparatively equal positional accuracy of 10-15 m (0.3 – 0.5 pixel), could be located on the
images with minimum ambiguity
Radiometric Normalization and image Differencing
Radiometric normalization needs targets having the properties of constant reflectors. A constant reflector
is the areas which are not covered by vegetation and so any change in brightness values are due solely
to non-surface factors. Forty single-pixel targets were selected according to criteria stated in Heo and
Fitzhugh (2000). Most developed areas cluster along the coast line making the choosing of normalization
targets relatively easy while much lesser targets could be identified in the agricultural-dominant inland
areas. As a result, the distribution of normalization targets substantially biases towards the coastal areas
as seen in figure 1. However, trying to incorporate more unsuitable targets to compensate the shortfall of
the appropriate ones is not a good strategy and since the study is part of the project which pay special
attention to map and monitor what are happening along the coast, this situation is considered as a favor
given to priority areas.
Scatter plots of brightness values in TM band 1 – 4 of the targets is shown in figure 2. In every band, the
range of brightness values covered by targets are good and it can be seen that strong positive
correlation exist in linear form between brightness values of the 1988 and those of the year 2000 image.
The coefficient of determination (R 2
) ranges between 77.6 % in Band 3 to 84.3 % in Band 4. Statistical
test at 95 % confidence interval of the linear correlation coefficient (R) using Pearson critical value of
0.312 (Triola, 1995) concludes that there is a significant linear correlation.
Figure 1. Normalizations targets in the bottom half of the year 2000 Landsat 5 image.
Figure 2. Linear regression analysis of TM band 1 – 4.
The slope and coefficient parameters determined from least square linear regression were used to
normalize the year 2000 image’s brightness values to the year 1988 using ERDAS 8.4 software. The
normalized year 2000 image was then subtracted pixel by pixel from the year 1988 image and produced
the differenced image whose brightness values are in floating point number. Image resulting from
differencing can be classified to produce change image or change map. Theoretically, areas in change
maps whose values are greater or lesser than zero are considered as changed but taking into account
the factors such as perfect co-registration and normalization cannot be achieved a tolerance is required.
A difference image tends to have a histogram that is normal in shape with the peak at or near zero and a
long tail extending towards the higher values (Mather, 1999). The mean and standard deviation of the
difference image is shown in table 1. Different strategies to choose the suitable threshold exist but an
intuitive one would base on the statistical characteristic of pixel values in the difference image. A method
suggested by Fung and Ledrew (1988) is to select threshold values at +/- N standard deviation from the
mean where N is a small number like 0.1. Accuracy is determined and the threshold value is increased
by N for the next round of classification. This is done repetitively and results from each iteration are
tabulated for accuracy assessment. The optimal threshold value is the one which provides most accurate
result. The method of Fung and Ledrew (1988) is slightly simplified and adopted for this research. The
simplification is done by using single threshold value for every band instead of one for each band. The
threshold value is set at the increment of 5.0 which is approximately half the average of standard
deviations in four bands. Another simplification made is to threshold the difference image from zero
rather than from the mean of each band. This is done because otherwise it would be possible and
difficult to justify for a pixel having zero value to be classified as ‘change’ if the mean differs significantly
from zero.
Table 1. Mean and standard deviation of the difference image.
Accuracy Assessment
One hundred and eighty check points were selected for the accuracy assessment of change detection.
The location of these points lie in the narrow strip of about 30 km wide running along the 200 km coast
line of Rayong and Chantaburi. To minimize the effect of positional error, each selected locations must
cover the area of at least a few pixel. Roughly one third of the ground truth points could be
unambiguously interpreted from the year 2000 image and the others were checked in the field. Next,
Visual interpretation on the old Landsat image was carried out to determine what these selected point
were in the year 1988. However, the majority of the selected locations could not be determined in this
way and so the 1:15,000 aerial photographs obtained from the Land Titling project taken in 1990 were
employed in the interpretation process. The position of these points together with the information of what
they were in 1988 and 2000 were used to construct an Arcview shape file. Finally, another attribute field
indicating if change really happened at a point was then derived. For the sake of convenience, the
difference image was transformed to the Arcview’s grid format file so that all subsequent work can be
carried out in a GIS environment
Figure 3. Ground truth locations superimpose on change map produced by different threshold values,
above left) 5, above right) 10, low left) 15, low right) 20.
With the aid of verified locations, the accuracy of change detection was determined in the following way.
