In: Wagner W„ Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Voi. XXXVIII, Part 7B 
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layer and the 7 output layer nodes were defined based on the 
number of training site categories. For balance between training 
time and overall error reduction, learning rates between 0.01 - 
0.2 were used, and to reduce the oscillatory problems, 
momentum factor between 0.5 and 0.6 were applied. To 
terminate the training process, the accuracy rate was set to 90% 
and 10000 iteration were chosen. 
The more appropriate network parameters considered in this 
study were shown in table 2. For comparing the MLP results of 
IARR, PCA and MNF inputs this structure was used separately 
for each input. Three Hard classification images based on 
different input were produced by MLP approach to showing the 
lithological units and distribution of salt plug-affected areas 
(Fig 3). 
Parameter 
Value 
Hidden layers 
8 
Learning rate 
0.10 
Momentum factor 
0.5 
Sigmoid constant a 
1.0 
Accurate rate % 
90% 
Table 2. The more appropriate network parameters that were 
used in this study. 
Figure 3. MLP mapping results, (A) result of IARR input to 
MLP, (B) result of PCA input to MLP, (C) result of MNF input 
to MLP. 
4. ACCURACY ASSESSMENT OF INPUT DATASETS 
To evaluate the results of MLP classification maps obtain from 
three input datasets (IARR, PCA and MNF), the accuracy of 
salt-affected areas and lithological units were assessed using 
ground reference information by determining the overall 
accuracy and Kappa coefficient. 
The accuracy results are summarized in table 3. Comparison of 
the results of different input datasets shows higher capability of 
the MNF input to detect salt-affected areas. The accuracy 
results indicated that operating MLP with MNF input has higher 
accuracy (85%) than the IARR input with 79% accuracy and 
PCA inputs with 82% accuracy, so the hard classification image 
produced by MNF input was used to assessment of the salt 
plug-affected areas. 
Input to MLP 
Overall 
accuracy 
Kappa 
coefficient 
IARR 
79% 
0.64 
PCA 
82% 
0.77 
MNF 
85% 
0.79 
Table 3. Accuracy assessment of hard classification maps 
5. RESULT AND DISCUSSION 
This study investigated the utility of the MLP network with 
different input (IARR, PCA and MNF) for detecting salt plug- 
affected areas southeastern Shiraz, Iran. 
The ability to map salt plugs, and extent of the salt plug’s 
materials are essential to understand and minimize salt plug’s 
environmental impact and provides practical solutions to more 
advantageous water resources management. 
At first, ASTER datasets of the area were analyzed by using the 
PCA method. By this method, the ASTER data were limited to 
3 bands. Table 1 shows PCA eigen analysis of ASTER image. 
These components were used to find suitable training areas for 
the classification, as well as gathering sufficient number of 
training samples for each lithological unit with the aim of 
existing geological map. The training areas were used to 
training the MLP neural network and detecting salt plug- 
affected areas. Hard classification image of MNF input to MLP 
provide the opportunity to map salt plugs and salt plug-affected 
areas, as well as to estimate extent of salt plug materials. This 
may be important to identify impacts of salt plug on the 
adjacent areas, especially on the Firouzabad River (Fig 4). 
The Jahani (central part of the scene), Konarsiah (upper part of 
the scene) and Kohe Gach (western part of the scene) were 
identified from this neural network method. The Hard 
classification image of the southern Firouzabad show Konarsiah 
salt plug in elliptical shape. This salt plug is located at the top 
of the image, surrounded by salt plug-affected areas along the 
slopes and margins of the salt plug (Fig 4). Yellow boxes in 
figure 4 indicate polluted areas surrounding the Konarsiah. The 
spatial distribution of salt plug-affected areas observed in the 
hard classification image revealed three main spatial trends. 
Relatively, high distribution of salt plug materials is seen in the 
northern, southern and western parts of the Konarsiah. It seems 
that morphology of the salt plug plays a major role on the 
shaping of the salt plug-affected areas, because it controls the 
flow of surface runoff and hence the distribution of salt plugs 
materials. The main tributaries that convey water as well as 
Konarsiah salt plug materials are shown in figure 4, drainages 
1, 2 and 3, including branches that convey the Konarsiah 
materials into the Firouzabad River. The branch 1, located at 
the northern Konarsiah salt plug, drains its materials toward the 
east, but branches 2 and 3 are situated in the eastern and 
western sides of this salt plug respectively, draining their 
materials toward the south. 
The Jahani salt plug is located at the center of the hard 
classification image (Fig 4). High distributions of salt plug 
materials occur in the eastern, western and south western parts 
of the Jahani. This image shows that the salt plug materials are 
extending down to the Firouzabad River. The amounts of 
materials decrease from the salt plug to the Firouzabad River. 
The results show a good differentiation between salt plugs 
materials and other lithological units however, some 
misclassifications occur in south east Jahani salt plug due to the 
spectral similarities. The main tributaries that convey the water 
as well as the Jahani salt plug materials are shown in figure 4.