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Proceedings of the Symposium on Global and Environmental Monitoring

Haruhisa SHIMODA, Kiyonari FUKUE, Tukasa HASHINO,
and Toshibumi SAKATA
Tokai University Research and Information Center
2-28-4 Tomigaya, Shibuya-ku, Tokyo 151, Japan
A new automatic landcover classification method for multi temporal data is presented
in this paper. This method is based on prior condition that a landcover map of the
target area already exists, or at least one of the multi temporal data had been
classified. The landcover map or the classified result is used as a reference map to
specify training areas of classification categories. The new classification method is
consist of five steps, i.e., extraction of training data using the reference map,
change detection based upon the homogeneity of training data, clustering of image data
within areas where changes were detected, reconstruction of training data using
overall image data, and maximum likelihood classification.
In order to evaluate the performance of this method, temporal Landsat TM data were
classified by using this method and a conventional method. As a result, we could get a
landcover change with high reliability and fast throughput, without a skilled
Key Words; multi temporal data, change detection, landcover classification,
automatic training data selection
Since the launch of Landsat-1 on 1972,
enormous observation data have been
integrated in various organizations such
as receiving stations, institutes, and
application users. Many studies have been
trying in order to utilize those data
with more effectively, e.g. EosDIS(Earth
Observing System Data and Information
System) and GIS(geographical Information
System). It is assumed that processing
technology for multi temporal data have
become important from a view point of
above the present condition.
Utilization objectives of multi temporal
data can be divided into two categories;
(1) to improve analyzing accuracies, and
(2) to analyze change of the earth surface
such as land-cover/use, and sea surface
temperature. This research presented in
this paper is concerned about the later
objective, especially for land-cover/use
Following two methods have been usually
used as change detection methods of land-
cover/use; (l)a method detecting change
areas from observation image data
directly, and (2)a method detecting those
from multi temporal thematic maps. In
general speaking, the first methods can
not detect change areas exactly. One of
the reasons due to that it is difficult
to compensate influences of variation
about observation conditions such as sun
elevation, atmospheric condition, and so
on. The other one depends on that
spectral features of a land-cover/use may
change according as seasonal changes,
e.g. leaf color of forests, grass
rankness and water contents of bare
grounds, growing situation of crops, and
etc.. On the other hand, the second
change detection method has potential to
eliminate those problems.
However, there are several problems on
conventional supervised methods of land-
cover classification for a set of multi
temporal image data. First, training data
must be extracted by a skilled operator
for each temporal image in order to get
high classification accuracies. Second,
the operator should perform ground truth
before training data extraction. As well
known, ground truth and training data
extraction are highly time consuming
processes. Third, it is difficult to
extract sufficient training data using
supervised method especially for high
ground resolution sensor data such as
Landsat TM and SPOT HRV, because of large
variation of image level with pixel-by-
pixel/1,2/. For instance, value of pixels
in low density urban areas have very
different values by a reason which those
area are consisted of many kinds of
landcovers, e.g. house roofs with several
colors, lawns, trees, concretes,
asphalts, bare grounds, etc.. Further, it
is difficult to analyze landcover changes
because of unstable classification
accuracies for each temporal data. A
method overcoming these problems for
conventional supervised classifications
is presented in this paper.
The major problems on supervised methods
of landcover classification for a set of
multi temporal image data are induced
from supervised training data extraction,
as described at previous section. There
fore, in order to solve those problems,
it is necessary to automate the process
of training area selection and adjusting
of training data according to each multi
temporal image.