In: Paparoditis N., Pierrot-Deseilligny M.. Mallet C.. Tournaire O. (Eds). 1APRS. Vol. XXXVIII. Part 3A - Saint-Mandé, France. September 1-3. 2010 
119 
For compression, the wavelet-based compression was used and 
the effectiveness of different wavelet families were examined. Fi 
nally, the biorthogonal CDF 3/9 was chosen with the best com 
pression rates for this problem. The wavelet coefficients were 
thresholded first (with a value of 5.0) to discard the small co 
efficients representing unimportant features, and then a uniform 
mid-tread quantizer with 255 levels and RLE was applied to the 
data. With this method a compression rate of 0.21 was achieved, 
i.e. the data was compressed to less then one quarter of the origi 
nal size, with the reconstruction error having a standard deviation 
of only 1 intensity value. 
Both the original and the reconstructed waveform data was classi 
fied using an unsupervised classification method, the based on the 
SOM algorithm, and the efficiency of the waveform data to use 
for classification purposes was examined before and after com 
pression. 
Correlation was observed betw een the shape of the waveforms 
and the backscattering material. After the waveforms were sep 
arated into one- and multiple-echo waveforms, statistical param 
eters of the one-echo waveforms were calculated for the SOM- 
based classification (standard deviation, skewness, kurtosis and 
amplitude). As a result, the observation set was divided into three 
subsets, corresponding to trees, grass and non-vegetation areas. 
The classification was enhanced by separating the non-vegetation 
areas into pavement and roof, using the local range differences, 
calculated from the center of mass values and the starting time of 
the backscattered waveforms. 
Visual comparison of the result of the classification with aerial 
imagery show's, that full-w'aveform LiDAR data can be used very 
efficiently to separate the different types of surfaces. Numerical 
verification shows a success rate of 84.9%. 
Comparing the classification of the original and the reconstructed 
waveforms, 9.7% misclassification was observed. Further devel 
opment of the compression and the classification algorithm is ex 
pected to overcome this difficulty. 
ACKNOWLEDGMENTS 
The authors thank to Optech Incorporated for the data provided 
for this research. The second author wish to thank to The Thomas 
Cholnoky Foundation for the support of her visit at The Center 
for Mapping at The Ohio State University, during the time-period 
this work has been accomplished. 
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