
%*********************************************
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\chapter{Project specification}
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\section{Satellite Image Classification}

Since mankind is able to take pictures from outer space, it has
always been a difficult task to recognize specific patterns,
related to a special problem.
As computer science raised a stadium where computers are able to
perform some "intelligent" tasks, a wide research area established
in solving the problem of automatic image classification.
There have been many different methods of resolution, ranging from
genetic algorithms to neural networks.
One common problems in all these attempts is that applications of
biological methods are extreme time intensive. \\
Nowadays distributed systems are well known and popular used, so
it's the logical consequence applying this technology to the
problem domain of image classification.
One possible way achieving a collaboration of these technologies
is by using a multi agent system.


\subsection{Data Structure}

One important characteristic of satellite images is the wavelength
used for sampling. Each wavelength range is distinguished by
determined advantages and disadvantages, which places it best in certain
application areas. Table \ref{tab:WAVELENGTH} lists the most
common used wavelength ranges, which was taken from \cite{UN98}.

%
% Tabelle als Gleitumgebung
\begin{table}[htb!]
\caption{Wavelengths in the electromagnetic spectrum routinely
used for various remote sensing applications}
\label{tab:WAVELENGTH}
\begin{center}
\begin{tabular}{|p{2.5cm}|p{4cm}|p{8.5cm}|l|l|l|}
\hline
 Wavelength range  &  Notation  &   Application related areas  \\
\hline \hline
0.40-0.50 $\mu$m   &  blue      &   Water penetration and water depth\\
\hline
0.50-0.60 $\mu$m   &  green     &   Vegetation greenness, ocean colour\\
\hline
0.60-0.70 $\mu$m   &  red       &   Chlorophyll absorption in healthy plants, iron oxide
                                content in soils, water sediment load\\
\hline
0.70-0.90 $\mu$m   &  near IR   &   Healthy vegetation response, crop monitoring and
                                  classification, land and water separation; vegetation
                                  and soil separation; separation of built and
                                  vegetated surface cover  \\
\hline
1.55-1.75 $\mu$m   & near IR    & Soil moisture content\\
\hline
2.00-2.40 $\mu$m    & SWIR      & Presence of clay-based minerals\\
\hline
3.00-4.00 $\mu$m    & mid IR + thermal IR & Volcanic activity, bush fires, underground fires\\
\hline
9.00-12.50 $\mu$m   & far IR + thermal IR & Earth ocean and land temperatures\\
\hline
2.4-3.75 cm         & X-band microwave & Forest canopy shape, crop classification. Ocean
                                        roughness,wind speed\\
\hline
3.75-7.5 cm         & C-band microwave & Crown thickness, leaf/branch size and orientation.
                                       Plant morphology. Ocean surface roughness, wind
                                       speed; oil seeps; surface elevation; land
                                       cover; bathymetry; geology; gravity fields;
                                       sea-ice and iceberg monitoring\\
\hline
15-30 cm            & L-band microwave  & Trunk size and tree density; ocean roughness;
                                          soil surface roughness; soil
                                          moisture content; forest clearcut areas\\
\hline
30-100 cm           & P-band microwave  & Trunk size and tree density; soil moisture
                                        content; soil surface penetration and under
                                          surface phenomena; snow penetration\\
\hline

\end{tabular}
\end{center}
\end{table}

\newpage

\subsection{Sample Images}

In our problem domain the used wavelengths are the X- and the
P-Band. Pictures \ref{fig:X_BAND} and \ref{fig:P_BAND} represent
the same geographical area, where picture \ref{fig:X_BAND} was
sampled in the X-Band and picture \ref{fig:P_BAND} in the P-Band.

%***** Include a picture *****
\begin{figure}[htp!]
    \begin{center}
        \footnotesize \footnotesize
        \ifpdf
            \includegraphics[width=1.0\columnwidth]{jpeg/x_band.jpg}  %Filename.jpg
        \else
            \includegraphics[width=1.0\columnwidth]{eps/x_band.eps}  %Filename.eps
        \fi
        \caption{X-Band example}
        \label{fig:X_BAND}
    \end{center}
\end{figure}


%***** Include a picture *****
\begin{figure}[htp!]
    \begin{center}
        \footnotesize \footnotesize
        \ifpdf
            \includegraphics[width=1.0\columnwidth]{jpeg/p_band.jpg}  %Filename.jpg
        \else
            \includegraphics[width=1.0\columnwidth]{eps/p_band.eps}  %Filename.eps
        \fi
        \caption{P-Band example}
        \label{fig:P_BAND}
    \end{center}
\end{figure}

\newpage


\subsection{Example for a Classification}

Find a properly visualization of such classifications during research is always
difficult and time consuming. Therefore using some powerful tools
is a duty. In our case YARB \footnote{{\bf Y}et {\bf A}nother
{\bf R}esult {\bf B}rowser}, an ultimate visualization system, fitted
best.\\
\noindent
In picture \ref{fig:YARB_SAT_MAS} you can see a classification of
such a satellite image printed with YARB.


%***** Include a picture *****
\begin{figure}[htp!]
    \begin{center}
        \footnotesize \footnotesize
        \ifpdf
            \includegraphics[width=1.0\columnwidth]{jpeg/YARB_sat_mas.jpg}  %Filename.jpg
        \else
            \includegraphics[width=1.0\columnwidth]{eps/YARB_sat_mas.eps}  %Filename.eps
        \fi
        \caption{Sat-Example (Viewed with YARB)}
        \label{fig:YARB_SAT_MAS}
    \end{center}
\end{figure}


\newpage


\section{System Architecture}

\label{Sect:SysArch}

There are many boundary conditions to be thought of, and we will
discuss every part of them in a separate section:

\begin{itemize}

\item   Multiple agents

\item   Local experts, ability to make  requests for reinforcement

\item   Autonomous movement of agents

\item   Controller

\item   Distributed system, communication

\item   World modeling


\end{itemize}


\subsubsection{Agents}

The system should be built of different agents. Each agent has
its own operational area and should have a high degree of
autonomy. It is free to move around its area and collect data
samples, which is used to learn the associated classification neural network.
But there is not only a NN for classification, the movement
control of the agent is also realized by a neural network.\\
The gathered data samples are hold in a "bag pack", that has a
limited capacity. The decision which data samples are valuable is made by the agent
itself.

The expertise of an expert should be convergent to the maximum,
but when the data distribution of its area is too wide spread,
this can't be guaranteed. In this case the expert has the ability
to ask for reinforcement so that his area will be split into a
distinct number of sub areas and each area will be inspected by a
own agent.
To prevent the loss of the old agents gained experience a nice
extension would be to implement mechanism for transferring
knowledge between agents. There are already research approaches on
such mechanisms, like the one in \cite{quir00}.



\subsubsection{Controller}
After the learning period of all the agents is finished, the
user may ask the controller for the classification results. The
responsibility of the controller is then to ask the agents, which
are experts of a distinct area, and bring together the corresponding results.



\subsubsection{Distribution and communication}
One boundary condition is to decrease the time consumption needed
for full classify a satellite image. This is done by distributing
the agents to multiple computer systems. Therefore a stable and
efficient communication system is needed. As the preferred
implementation language is Java, the method of choice will be RMI.



\subsubsection{World modeling}

Using different frequency bands leads to a big problem for system
modeling: Should the agents move on a discrete area filled with
data samples or on a net based on the real measure
points? The advantage of the former method is that agent movement
is trivial and it is easy to determine the experts of a specified
area. The problem would be that there wont be a data sample on
each point of the square grid, and even large areas of the grid
may have almost no samples. As illustration of this problem take a
look at picture  \ref{fig:YARB_SAT_MAS}, where the mass of data samples
are only in a relatively small zone.

Another approach would be to create a net consisting only of the
sample points. But then it would be difficult to calculate the
movement of an agent and also find the best experts of an area.
