\documentclass{isprs}
\usepackage{subfigure}
\usepackage{setspace}
%\usepackage{isprs}


\begin{document}

\title{LaTeX and BibTeX template for ISPRS publications}

\author{Marc Bartels and Hong Wei}

\address
{
	School of Systems Engineering, Computational Vision Group\\
	The University of Reading\\
	Whiteknights, Reading RG6 6AY, UK\\
	m.bartels@reading.ac.uk, h.wei@reading.ac.uk\\
	http://www.cvg.reading.ac.uk/projects/LIDAR

}

\workinggroup{I/2, III/1, III/4, III/5}

\abstract
{

Dear author preparing publications for the ISPRS, you may use this template, however, it is strongly recommended to consider the ISPRS guidelines as listed on the homepage of the ISPRS: http://www.isprs.org/documents/orangebook/app5.html

}

\keywords{LIDAR, point cloud, object, ground, unsupervised segmentation, skewness}

\maketitle

\section{INTRODUCTION}\label{INTRODUCTION}

\mbox{LIDAR} data is being used more and more by the public sector and commercial world since the early 1990s~\cite{maas2005}. \mbox{LIDAR} data point clouds are accurate for less hilly terrain~\cite{huising1998}. Geometric primitives were estimated based on histogram analysis of surface normals~\cite{haala1997,haala1998ohio}.

\section{SEGMENTATION ALGORITHM}\label{sec:SEGMENTATIONALGORITHM}

\subsection{Theoretical Background}\label{sec:TheoreticalBackground}

The arithmetic mean $\mu_a$ is defined in Equation (\ref{equ:mean})

\begin{equation}\label{equ:mean}
	\mu_a = \frac{1}{N} \cdot \sum\limits_{i = 1}^{N} s_i
\end{equation}

where $N$ is the total number of the \mbox{LIDAR} points $s_i$, with $i \in \left\{1,2,\ldots,N\right\}$.


\subsection{Proposed Algorithm}\label{sec:ProposedAlgorithm}

Another subsection \ldots

\begin{table}[h]
	\centering
		\begin{tabular}{|c|c|c|c|}\hline
			Test 1&Test 2&Test 3&Test 4\\\hline
			1&2&3&4\\
			10&20&30&40\\\hline
		\end{tabular}
	\caption{Example 1 of a table}
	\label{tab:table_example}
\end{table}

As listed in Table \ref{tab:table_example} and \ref{tab:Producer_User_Accuracy}, respectively \ldots

\begin{table}[h]
	\centering
	\small
		\begin{tabular}{|c||c|c|}\hline
			Category&Producer&User\\\hline\hline
			A&$82.96\%$&$86.54\%$\\\hline
			B&$88.62\%$&$86.01\%$\\\hline
			C&$32.68\%$&$35.05\%$\\\hline
			D&$84.37\%$&$83.99\%$\\\hline
		\end{tabular}
	\caption{Example 2 of a table}
	\label{tab:Producer_User_Accuracy}
\end{table}

\newpage

\section{RESULTS AND DISCUSSION}\label{sec:RESULTSANDDISCUSSION}

The histogram of the \mbox{LIDAR} data tile in Figure \ref{fig:exampleFig} shows both classified ground (blue) and object (red) points. However, Figure \ref{fig:exampleFig} depicts the same histogram using subfigures in Figure \ref{fig:exampleFig_a} and \ref{fig:exampleFig_b}, respectively.

\begin{figure}[h!]
\begin{center}
		\includegraphics[width=1.0\columnwidth]{exampleFig}
	\caption{Histogram of classified points}
	\label{fig:exampleFig}
\end{center}
\end{figure}

\begin{figure}[h!]
	\centering
		\subfigure[Histogram 1]{
			\label{fig:exampleFig_a}
			\includegraphics[width=0.7\columnwidth, angle=270]{exampleFig}}
		\subfigure[Histogram 2]{
			\label{fig:exampleFig_b}
			\includegraphics[width=0.7\columnwidth, angle=270]{exampleFig}}
	\caption{Rotated histograms of classified points as subfigures}
	\label{fig:exampleFig_subfigure}
\end{figure}



\section{CONCLUSIONS AND FUTURE WORK}\label{sec:CONCLUSIONSANDFUTUREWORK}

Conclusions and future work.

\section*{ACKNOWLEDGEMENTS}\label{ACKNOWLEDGEMENTS}

The authors would like to thank Fabio Remondino\\(fabio@geod.baug.ethz.ch), Institute of Geodesy and Photogrammetry, ETH Z{\"u}rich, Switzerland, for the initial class and style files. Thanks also go to the organisers of the PCV 2006 in Bonn, Germany, for posting the class and BibTeX style files\\(http://www.ipb.uni-bonn.de/isprs/pcv06/).



{%\footnotesize
	\begin{spacing}{0.9}% tune the size by altering the parameter
		\bibliography{ISPRSguidelines_authors} % Include your own bibliography (*.bib), style is given in isprs.cls
	\end{spacing}
}

\end{document}
