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3. Classification of eighteen bands TM and 
three-illumination model data 
For this data combination, the topological 
structure of the local BPANN consists of 
twenty-one PEs in the input layer, which 
represent the eighteen band Landsat TM data, 
and three illumination model data corresponding 
to the May, August, and October TM data, 
respectively. The MANN was trained by the 
same group of training samples as used in the 
eighteen dimension case but had the illumination 
model data involved. The RMS error, network 
weight, Gating-Lrn, and  Gating-Rcl were 
monitored during the MANN training and 
classification. The trained MANN was applied in 
twenty-one dimension multisource spatial data 
classification. An overall classification accuracy 
of 91.79% was achieved. A balanced 
classification accuracy was achieved for all of the 
land cover categories. 
Classification Comparison and Neural 
Network Paradigms Evaluation 
1. Classification performance in eighteen band 
Landsat TM data 
In MANN, two "local networks" 
compete to learn the patterns inside the input 
vectors. Therefore, in high dimensional spatial 
data cases, land cover patterns inside the 
multispectral, multitemporal Landsat TM data 
could be effectively identified. The Gaussian 
maximum likelihood (GML) classification, on the 
other hand, will perform well if the input data are 
in perfect normal distribution and the prior 
probabilities for each land cover category are 
known. However in remote sensing data 
classification, Landsat TM data may not be 
perfectly normally distributed for certain land 
cover categories. Also, it is difficult to know the 
prior probability for each category before the 
classification has been conducted. Usually, an 
equal prior probability assumption is adopted in 
the application of GML. It is obvious that this 
assumption is not always a good one, particularly 
when the area is dominated by certain land cover 
categories, such as large forest coverage and 
small residential areas. Those factors may effect 
the classification performance of GML. 
2. Classification performance in nineteen- 
dimension multisource spatial data 
In nineteen dimension multisource spatial 
data cases, the transportation data were involved 
in pattern recognition and classification. The 
transportation data, in nominal data type, has no 
meaningful statistical mean and covariance 
matrix. Therefore, the transportation data could 
not be effectively classified by GML. The 
distribution-free capability of artificial neural 
networks, on the other hand, made the MANN 
flexible in input data selection. The classification 
assessment indicated that MANN paradigm 
achieved good classification results. Visually, all 
of the details of transportation lines were 
successfully identified. 
3. classification performance in twenty-one 
dimension multisource spatial data 
The twenty-one dimension multisource 
spatial data include eighteen multispectral, 
multitemporal Landsat TM data and three 
illumination models. Accuracy assessment results 
(Figures 3) show that by having the illumination 
data involved, the variability of classification 
accuracy on different categories was reduced. In 
particular, a relatively stable and balanced 
classification accuracy was achieved for all of the 
categories by MANN. 
CONCLUSIONS 
1. MANN has the abilities to handle multisource 
spatial data. In MANN, there is no constraints 
about statistical distribution and measurement 
scale of different source data. 
2. MANN apply the combination of local 
networks and the gating network in pattern 
recognition. MANN assign input data to 
different local network and adjust the output of 
local network by the gating network. This 
mechanism is effective in high dimensional 
multisource spatial data classification. Since two 
or more local networks compete to learn the 
input data patterns, more details among closely