and N,,,,,, representing the number of PEs in the 
input, hidden, and output layers, respectively, the 
geometric pyramid rule defines that: 
NHidden = N Input X N output 
A more general approach applied here to 
determine the optimal number of hidden PEs was 
to start with a small number of hidden PEs, 
increasing the number of hidden PEs, and 
training and testing the network performance 
again. This was repeated until the error was 
acceptably small, or no significant improvement 
was noted. As result, a hidden layer with 
twenty-two hidden PEs was adopted as the 
architecture of the local BPANN. 
The gating network has four hidden PEs 
and two output PEs. The two gating output PEs 
represent the gating output for the two local 
BPANNs. The gating network controls the 
competition and learns to assign different regions 
of the data space to different local expert 
networks. The selected training samples was 
used to train the MANN. The trained MANN 
was then used to classify multisource spatial 
data. Three data combination cases were 
considered and tested. 
1. Classification of eighteen band Landsat TM 
data 
The above architecture was applied in the 
eighteen band multispectral, multitemporal 
Landsat TM data classification. During the 
networking training, several ^ monitoring 
procedures were applied. One was to monitor 
the RMS error between the desired output and 
the response of the network. The monitoring 
window plots the RMS error of the output layer. 
As learning progresses, the error graph should 
converge to zero or asymptote near zero. The 
monitoring procedure also include the network 
weights distribution, in which a normalized 
histogram showing the weights going into the 
output layer is displayed. A normal distribution 
to these weights indicates that the network is 
performing well. In addition to the RMS error 
and network weight, two more monitoring 
procedures were added. One procedure named 
Gating-Lrn was used to monitor the gating 
activity during network learning. In Gating-Lrn, 
the outputs of the gating network are shown on 
strip chart. The outputs initially show fairly 
constant graphs, but as the gating networks start 
to learn to allocate responsibility to different 
local networks, the graph begin to oscillate as 
input data are randomly presented. The other 
procedure named Gating-Rcl was used to 
monitor the gating activity during network 
classification. In Gating-Rcl, output of the gating 
network is shown as a bar graph, each bar 
corresponding to an individual local network. 
For each input vector through the network, the 
size of each bar shows the contribution of the 
corresponding local network to the composite 
output. Hyperbolic tangent transfer function was 
adopted. As an experimental protocol, 100,000 
iterations were adopted. The trained MANN was 
then applied in the eighteen bands Landsat TM 
data classification. Based on the reserved test 
data, an overall classification accuracy of 
93.11% was achieved. The Kappa index of 
agreement indicated that a good classification 
(over 80% classification accuracy) was achieved 
for most of the categories. 
2. Classification of nineteen-dimension TM and 
transportation data 
The MANN in nineteen-dimension data 
classification was designed to have almost the 
same architecture as the one in eighteen band 
Landsat TM data classification, except that each 
local BPANN network had nineteen PEs in the 
input layer. The nineteen input PEs represent the 
eighteen input Landsat TM band data, and one 
input from the transportation data. The MANN 
was trained by the same group of training data as 
used in eighteen-dimension cases but also had 
the transportation data involved. The same 
monitoring procedures were used. The trained 
MANN was applied in the nineteen-dimension 
multisource spatial data classification. Based on 
the reserved test data, an overall classification 
accuracy of 91.27% was achieved. Visually, all 
of the transportation lines appeared successfully 
classified. 
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