Assessment of mapping
accuracy of Landslides using image classification techniques
Scott L. Huang, Been K.
Chen, Robert C. Speck Department of Mining and Geological
Engineering University of Alaska Fairbanks, Fairbanks, Alaska 99775,
U.S.A.
Abstract Lnadsat-5 TM scene images
of Healy, Alaska and terrain information (i.e. elevations, drainage
system, bedrock formation, and geological structures) were processed using
minimum distance, parallelepiped and Bayesian classifiers. Among the three
methods, the Bayesian classification with a threshold value of
10-1 revealed the best mapping accuracy of 16.60%.
Introduction In the past two decades, remote sensing
technologies including use of aerial photographs and satellite imageries
have been applied widely to regional landslide investigations. Simonett et
al. (1970), Scully (1973), Mc Donald and Grubbs (1975), Anderson et al.
(1976), and Sauchyn and Trench (1978) relied on visual interpretation for
classification of landslide phenomena on either aerial photographs or
satellite images. Remotely sensed data along with image interpretation can
provide terrain information pertaining to Landsliding (Gagon, 1975). Those
important factors for assessment of landslide potentials regional
physiography, geomorphology, and geological structures.
With an
improvement of computer technology, research undertaken by Heath and
Dowling (1980) and Stephens (1988) applied digital image processing to
delineate landslide areas on satellite images. Certain terrain information
such as elevations, drainage patterns, bedrock formations, and geological
structures are valuable for predicting Landslides and this information,
however, could not be obtained directly from image interpretation.
Therefore, in the study the authors attempted to identify areas in Lignite
Creek coal basin, Healy, Aaska (Figure 1) where Landslides are likely to
occur by taking advantage of the terrain information while processing
digital satellite images. The intention of this research was to assess the
reliability of image classification techniques in the study of Landslides.
Large number of Landslides that occurred in Lignite Creek basin, Healy,
Alaska influence surface mining operation of a coal mine in the vicinity
(Corser and Paker, 1987) . Prior knowledge of the potential Landslides can
often permit more flexible and accurate design of a mining method to
minimize financial risk and unnecessary engineering problems associated
with slope movements.
Landsat TM-Images A Thematic
Mapper (TM) scene image (Figure 1) was acquired by Landsat -5 on September
22, 1984 (scene ID Y5020520430x0). The digital image was later loaded on
the ADVAL VAX 11/750 computer at the University of Alaska Fairbanks. The
scene's geographic center is at latitude N64°14'00" and longitude
W147°58'00". The entire coverage includes about 4,300mi2
(10,500 km2) in the interior of Alaska.
The Land
Analysis System (LAS) modules including Cooredt, Trancoord-II2utm,
Tiemerage, Nullcorr, Tiefit, and Geom were performed to register the TM
images to a common Universal Transverse Mercator- based (UTM) grid. In the
registration process, twelve control points were chosen from a topographic
map. The pixel size of the images was reformatted from 30 meters to 25
meters to increase geographic precision (Goodenough, 1988), and pixel
values were resampled using the nearest neighbor (NN) method.
Figure 1. TM images with existing
landslide deposits Terrain Information A Digital
Elevation Model (DEM) image of the Healy quadrangle was utilized as the
original spatial data to generate elevation contours (DNCon)
Percent slope (DNSlP) and slope aspect (DNasp)
images through LAS's Topo modules.
The existing landslide
deposits, bedrock lithologies, drainage system, and faults were digitized
from a geologic map through AMS to generate spatial terrain images,
designated as DNeld , DNgrp, DNflt,
respectively . The images were then referred to the same UTM grid
coordinates as that for TM's and the elevation derived images. Both
drainage system (DNdrn) anf faults (DNflt) images were binary
images composed of two digital values (i.e. 50 and 200 for better
contrast) Binary images were created by running LAS's Filter module to
dilate the linear boundary to a distance of 5 to 45 pixels on either side
of the trace. The existing landslide deposits (DNeld) image was
a binary image as well, with digital value 50 representing non-landslide
deposits and digital value 200 indicating existing landslide deposits. The
bedrock lithology image (DNgrp) was comprised of eight rock
units based on berrock formation in the area.
Image
Classifications
- Minimum Distance Classifier
In this classification, the
existing landslide deposits (Figure 1) were considered as training area.
In the other words, existing landslide deposits were used to compile a
numerical " interpretation key" that described the digital values of
Landslides in each input parameter image. Each pixel in the image was
then compared numerically to the interpretation key and labeled with
either landslide or non-landslide class. To do the analysis, the minimum
distance classifier was employed to make this comparison between unknown
pixels and the interpretation key pixels.
In this task, the
authors first took six Landsat TM-images as input for minimum distance
classification then the six terrain images were added to the TM's to
create the second set of input images. This allowed an evaluation of the
improvement of landslide classification over the initial TM-images.
- Parallelepiped Classifier
In the parallelepiped
classification, the ranges of values in each class may be defined by the
lowest and highest pixel values in each image. In this study, the range
of landslide class was defined by dividing the values (0 to 255) of TM
images into 3,5, and 7 intervals, although other intervals could be
chosen based on different algorithm and computer capacity. The otimal
range of landslide class for each input parameter image and its mapping
accuracy were obtained through computer search.
- bayesian Classifier
Unlike equal weighting for input images
in parallelepiped and minimum distance classifications, Bayesian
classifier assumes that the importance of each input parameter image is
unequal in terms of construction to an event (i.e. landslide event B).
Bayesian theorem, introduced by Thomas Bayes in the 1800's, is a
statistical approach concerning conditional, prior and posterior
probabilities for inferential and decision-making procedures. It was
applied in the study to calculate probabilities of Landslides.
In this study, the landslide deposits shown in the geological
map were considered as the existing occurrences of Landslides for later
prediction of the potential landslide area. The ratonale of applying
Bayesian the Orem here was to revise the prior probabilities P
(A1) (i.e. existing Landslides) to posterior probabilities P
(A1|B) (i.e. predicted Landslides) through available
information for predicting new or undetected Landslides.
Results and Discussion
- Minimum Distance Classification
Twenty training areas
consisting of the digitized landslide deposits were categorized as the
potential landslide class; the only class defined in this study. Of
those pixel values in images not lying in the range of potential
Landslides class were classified to non-landslide class. Prior to
executing the classification, the mean vectors of the six TM images and
the six terrain images were computed in order to calculate the minimum
distances (i.e. Euclidean distances) to class means for those input
images.
Two sets of input images were chosen, although there
could be thousands of combinations between those six TM images and six
terrain images. One set of the input images analyzed was the TM images
(dataset 1) , the other was all of the twelve TM and terrain images
(dataset 2). Defining a proper Euclidean distance, EDj , was
the pre-classification task. Different EDj resulted different
classification with differing accuracy of prediction. Among the accuracy
indices commonly used, mapping accuracy was applied to evaluate the
accuracy of classification Mapping accuracy, MA, defined by short (1982)
and Piper (1983), is usually applied to evaluate results for land cover
classification. The advantage of applying this index is that MA possess
the following characteristics equals zero if no positive match, equal
one if perfect matches, takes into account user's accuracy, and
producer's accuracy, and is not affected by sample size.
Figure
2 shows the results of minimum distance classification for six TM images
alone and the six TM images and six terrain images combined. As noted in
the diagram, the mapping accuracy of dataset 2 reaches its highest
accuracy of 12.19% as EDj becomes 120. The highest mapping accuracy of
dataset i was, however, much lower (i.e. MA = 3.65%) than that of the
dataset 2. Figure 3 shows the classified image of dataset 2 with
EDj equals to 120, which was the optimal result for both of
the input datasets.
- Parallelepiped Classification
The main task for applying
this classification was to define the ranges and logical operators
between images. As a result of the classification, out put binary images
showed the predicted landslide and non-landslide areas. The
classification was performed empirically on the
Figure 2. Euclidean distance vs.
mapping accuracy for datasets 1 and 2 Figure 3.
The classified image of dataset 2 with ED j of 120 (black:
predicted landslides, white polygons: existing landslides)
basis of visual quality of the processed images and
statistical characteristic of the training areas . The optimal
combination, which possed the highest mapping accuracy among TM and the
processed images, and terrain images was again obtained through computer
search. The following is the resulting algorithm from the search. Figure
4 shows the output image from this equation with mapping accuracy of
9.25%
{ ( 28 £ TM2 £37).AND.( 650 m £ CONTOUR
INTERVAL £ 850m).AND . {DRAINAGE = 1250 m
dilation AND (LITHOLOGY = coal-bearing)}
- Bayesian Classification
Table 1 lists the range of pixel
values of each of each input parameter image having the maximum
weighting factor (I.E. (w+- w-) The larger value
of (w+- w-) indicated the higher capability for
distiguishing Landslides and non-landsliding areas. The
DNflt, which was dilated by 35 two hihest values of (W
+ -W). This meant that the fault image, DNgrp,
were the two most important factors for istinguishing Landslides and
non-landsliding areas in Healy, Alaska . The
Figure 4. Landslide image predicted
using parallelepiped classification (black: predicted landslides,
white polygons: existing landslides) Table 1. Summary of
the optimal veighting factors for each input image
Pattern |
W+ |
W- |
W+-W- |
Pattern |
W+ |
W- |
W- |
TM1[60,68] |
0.6511 |
-1780 |
0.8291 |
DNslp [00,01] |
0.2569 |
-0.1326 |
0.3895 |
TM[22,31] |
0.5310 |
-0.3660 |
0.8970 |
DNasp[68,82] |
08026 |
-0.0318 |
0.8380 |
TM3[25,36] |
0.5018 |
-0.2735 |
0.7753 |
DNcon[14,14] |
1.3133 |
-0.4166 |
1.7300 |
TM4[37,50] |
0.3345 |
-0.1918 |
0.5263 |
DNflt[35,35] |
1,4677 |
-3,4357 |
4.9034 |
TM5 [21,24] |
0.0911 |
-0.0140 |
-0.1015 |
DNdra[35,35] |
0.5345 |
-1,9501 |
2.4846 |
TM7[19,19] |
0.1550 |
-0.0031 |
0.1581 |
DNgrp[06,06] |
0.7273 |
-4,1137 |
4,8410 | pixel values of the images
lying in the specified ranges listed in Table 1 were replaced by W+,
otherwise by W- for each image, to form a weighted image of itself.
Then, by applying Bayesian formula, which integrated posterior
probability of each input parameter image, the posterior probability
image was obtained. The pixel values of posterior probability image
ranged from 0 to 1. The two datasets which had been used in
parallelepiped classification were also chosen for creating posterior
probability images.
Figure 5 shows the posterior probability
image of dataset 2. The brighter area on the image indicates the higher
probability to landslide. Based on the histograms of posterior
probability images, various threshold values were chosen to creating
binary images showing lanslide and non-lanslide classes. Figure 6 shows
the variation of mapping accuracy vs. various threshold values selected
for both sets of input data. The best result of Bayesian classification
was obtained by processing both terrain and TM images with threshold
value of 10-1 (Figure 7) Conclusions Bayesian
classification, applying prior and posterior probabilities for unequal
weighting input parameter images, is more accurate than minimum distance
classification and parallelelepiped classification, which equally weighs
input images. With the terrain information added, the accuracy of all
three classifications can be much improved from that generated by the TM
images alone. The higher mapping accuracy indicates the more satisfactory
method for landslide prediction. Bayesian classification taking 10-1as a
threshold value has the highest mapping accuracy (i.e.16.60%)and is the
best result of this study.
Figure 5. Posterior probability images
of TM and terrain data Figure 6. Variation of
mapping accuracy vs. threshold values used in Bayesian classification
Figure 7. Posterior probability binary image of
TM and terrain data (black: predicted landslides, white polygons:
existing landslides) Acknowledgements The authors wish
to express their sincere gratitude to the Generic Mineral Technology
Center in Mine Systems Design and Ground Control, U.S. Bureau of Mines'
Office of mineral Institutes for the financial support of the study.
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