ization) overlaid
ing (b)
ION
In (a) the training
(green) pixels are
from a nearby but
on the algorithm
s used to train the
ch class (slide and
[he BNN has 36
neurons, and one
is a hyperbolic
n is Levenberg-
number of epochs
' testing area, the
whether that that
t of applying the
sure 6(a) showing
xels. The healthy
e 6 (b) shows the
1e shows the slide
; we compare the
area is correctly
'ea is classified as
n matrix for the
re classified with
r this testing area.
s slides has a high
jixels. Also, since
ny changes in the
vegetation can contribute to miscalculation of roughness and
detection as slides.
(b)
Figure 6. (a) Slide classification using the algorithm and
TerraSarX data; (b) The ground truth data for testing area
Table 1. Confusion matrix
rid Slide Hes Accuracy
Slide 486 463 0.5
Healthy Area 563 1616 0.7
Accuracy 0.5 0.8 0.67
5. CONCLUSIONS
Slough slides are slope failures along a levee. The roughness
and corresponding textural characteristics of the soil in a slide
can change the amount and pattern of radar backscatter.
Exploiting these changes in texture and backscatter pattern, an
algorithm based on a neural network and TerraSAR-X data is
developed to detect a landslide on the levee system. The
methodology includes: 1) segmentation of the levee area from
background and; 2) extracting features 3) training a back
propagation neural network classifier; and 4) testing and
validation of the results using ground truth data. A dual
polarimetric X-band image is acquired from the German
TerraSAR-X satellite on Sep 04, 2011. Ground-truth data
include the slides and healthy area. The results show that the
algorithm is able to detect the slide area with around 67%
accuracy. Since the TerraSAR-X is sensitive to changes in
texture and roughness and also vegetation, some pixels which
are not reported as slide zones are incorrectly classified as slide
pixels.
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