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Proceedings of the Symposium on Global and Environmental Monitoring

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A Method for Proportion Estimation of Mixed Pixel(MIXED
by Means of Inversion Problem Solving
Kohei Arai and Yasunori Terayama
Information Science Department
Science and Engineering Faculty
Saga University
1 Honjo, Saga-city, Saga 840 Japan
A basic idea of a contextual classification by means of a
method for proportion estimation of Mixed Pixel(MIXED featuring
Inversion Problem Solving technique together with a concept of a
Image Classification Artificial Intel 1igence(ICAI) with a
hierarchical multi-information integration are proposed.
An example with Landsat-5 TM data shows that estimated
proportions are useful as a contextual information when the pixel
of interest is MIXEL and is classified to an undesirable class in
the spectral and spatial feature space.
2. Basic concept of 1CA1 system with a hierarchical
multi-information integration
As well known, not only spectral and spatial
information derived from remote sensing satellite
imagery data but also relational information such as
inclusion, connection, etc. can be used in image
classification method. Fig.1 shows a hierarchical
structure of multi-information to be used for image
classification. In human perception process, three
layered multi-information are referred in almost
same time. The proposed 1CAI system should include
such information with flexible accessibility and
with traveling among the layers back and forth while
backward and forward reasoning should be capable.
Discontinuities of roads are sometime observed
in remote sensing satellite data. It depends on the
relationship between Instantaneous Field of
View(IFOV) and road width and/or the angle between
road and scanning directions. The pixels in the
discontinued portion of roads tends to be classified
as an improper class in such case because of their
properties in spectral and spatial feature spaces.
Contextual information is useful in such situation
together with information on mixing ratio or
proportion of each class in the pixel of interest.
Not so many papers dealt with proportion
estimation were published(Ref. 1 - 3). Namely the
method based on the feature mixing concept, the
method with a generalized inversion matrix, the
method based on the second order programming, the
least square mean of method and linear regressive
analysis with surrounding 8 neighbor pixels. The
proposed method is based on Inversion Problem
Solving method in a straight forward manner.
The proposed method is one of the elements of
the Image Classification Artificial
Intel 1igence(ICAI) with a hierarchical multi
information integration proposed herewith.
First, a basic concept on the ICA1 is described
followed by proposition of the proportion estimation
method using Inversion Problem Solving. Then an
example with Landsat-5 TM data of Saga, Japan is
For instance homogeneous segments may be
classified using only spectral information. On the
other hand, spatial information should be considered
for heterogeneous segments. It can be divided into
two categories, microscopic and macroscopic
properties. The former is based on relationships
between the pixel of interest and its surrounding
pixels while the latter is a spatial structure in
the image of interest such as road network, etc.
Contextual classification and spatial treatments are
useful for such pixels. Beside these relational
information can be also used.
Contradictions are sometime observed between
classified results obtained through spectral and
spatial classifications. For instance, the
aforementioned discontinued segments between
continuous road segments are classified as the
classes other than road class because of their
spectral signature. Sometime roads are covered with
crown of trees. Beside this, narrow width roads
compared to IFOV tend to be discontinued in the
image depending upon the angle between the road and
scanning direction, ratio of road width to IFOV,
etc. In such situation, contextual information
should be taken into account together with
relational information. The proposed ICAI system
allows us to refer such information including in the
hierarchical layered structure.