i, 1995. Changes
Lake Michigan
ym regional lake-
graphy. Vol 40,
, 1987. Lake ice
icator of climate
g using remote
F Climate Change
ydrologic Regime
of the Vancouver
mber 168.
<. Clayton, T.M.
ss. Determinants
ite-derived 1987-
n the Laurentian
d, 1993. Satellite
> indicator: Initial
i in Wisconsin.
! Remote Sensing.
L. Scarpace, and
-derived lake ice
Shield as a robust
mnual Convention
Charlotte, North
MODULAR ARTIFICIAL NEURAL NETWORKS: A NEW PARADIGM IN
MULTISOURCE SPATIAL DATA MODELING AND CLASSIFICATION
Yeqiao Wang
Laboratory for remote Sensing and GIS
Department of Anthropology (M/C 183)
University of Illinois at Chicago
Chicago, IL 60607-7138, USA
. Daniel L. Civco
Laboratory for Earth Resources Information Systems
Department of Natural Resources Management & Engineering
University of Connecticut
Storrs, CT 06269-4087, USA
ABSTRACT
Backpropagation artificial neural networks (BPANN) have been applied in remote sensing and
GIS data processing in the past several years. In this research, a new paradigm, modular artificial
neural network (MANN), was designed for multisource spatial data modeling and classification. The
modular design partitioned artificial neural networks into several subnets (individual BPANNs). Each
subnet act as a local expert responsible for interpreting a subset of multisource spatial data. A gating
network adjusts the output from each local network. While the unique information from different
data sources could be employed by individual subnets, the confusion and mixing within a single
source data could be filtered by the MANN. Therefore, the information from each data source could
be efficiently applied in spatial data modeling and land cover classification.
INTRODUCTION
Applications of artificial neural networks
(ANN) in remote sensing and multisource spatial
data classification have been frequently reported
in the past several years (Benediktsson, et al.,
1990, Ritter and Hepner, 1990, Liu et al, 1991,
Heermann and Khazenie, 1992, Civco, 1993,
Salu and Tilton, 1993, Hara ef al., 1994, Peddle
et al, 1994, Chen, et al, 1995, Foody et al,
1995). In the previous research, backpropagation
artificial neural network (BPANN) is the most
widely adopted paradigm. This popularity
primarily revolves around the ability of
backpropagation paradigm to learn complicated
multidimensional ^ mapping (Hecht-Nielsen,
1989). The advantageous of applying artificial
neural network in multisource spatial data
modeling and classification had been
demonstrated (Benediktsson, ef al., 1990, Ritter
and Hepner, 1990, Heermann and Khazenie,
1992, Civco, 1993). Other ANN paradigms,
however, are seldom applied and reported. At
current stage, BPANN are treated as black-box
tools. All of the initial or preprocessed
multisource data are submitted to BPANN for
solutions. Compared with using single date
remote sensing data, the input vectors are
expanded when multisource spatial data are
involved. Although improved classification
accuracy can be achieved (Hepner ef al., 1990;
Bischof et al., 1992, Civco and Wang, 1994), the
contribution from each individual data source,
however, is neither observed nor fully
93