Figure 5b Figure 5c 
Figure 4: Classification results (5b, 5c) of Area3 (5a) 
4. CONCLUSIONS AND FUTURE WORK 
In this paper, we introduce Sparse Representation framework to 
classify the high resolution airborne images and use DSM 
derived from LiDAR data to improve the classification. It's a 
pixel-oriented classification method and under the framework of 
Supervised Classification. The problem of ground object 
recognition is formulated as a Basis Pursuit problem and solved 
using convex programming methods 
in MATLAB. The key idea of this method is to represent the 
spectral vector (vector of IR-R-G value) of a pixel using 
observation matrix. The observation matrix consists of spectral 
vectors of pixels of typical ground objects (that is buildings, 
trees, vegetation and road) which are interactively selected on 
the images of test areas. A test procedure is carried out to 
examine the distinctiveness of the selected pixels and pixels 
which lead to misclassifications are replaced. In the recognition 
process, each pixel of images from test areas is classified using 
given observation matrix. Misclassifications often result from 
both steps of the classification procedure. Misclassifications of 
buildings and road in coarse classification procedure are mostly 
due to shadows and spectral similarity. Some low bushes in the 
shadow at left-bottom of Figure 2b are classified as buildings. 
All the buildings with similar color like road are misclassified 
as road (Figure 4b). In the procedure of refinement, 
misclassifications result from enforcement of only single 
elevation threshold on classification of ground objects. 
Adaptive methods that take account of local properties of 
ground objects and methods based on semantic knowledge may 
help improve the result. In our future work generality and 
effectiveness of our method will be further investigated and 
adaptive methods will be examined. 
Acknowledgement 
This research is supported by the National Natural Science 
Foundation of China (No. 40871211). The Vaihingen data set 
was provided by the German Society for Photogrammetry, 
Remote Sensing and Geoinformation (DGPF) [Cramer, 2010]: 
http://www.ifp.uni-stuttgart.de/dgpf/DKEP-Allg. html 
557 
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