Full text: ISPRS Workshop on 3D Virtual City Modeling (VCM 2013)

  
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume 1I-3/W1, 2013 
VCM 2013 - The ISPRS Workshop on 3D Virtual City Modeling, 28 May 2013, Regina, Canada 
15x15 Augmented WINN 
Quasi-exhaustive 
  
Figure 4: Ground truth (GT) and classification results of four different scenes using boosting (row-wise top to bottom: KLOTEN, GRAZ, 
VAIHINGEN, WORLDV IEW-2). Streets are displayed black, buildings red, high vegetation s: 
it, and low vegetation . KLOTEN 
  
and GRAZ show results of the entire test area without the nDSM channel, VAIHINGEN and WORLDVIEW-2 show zoomed details of 
the results using the nDSM channel. 
Leung, T. and Malik, J., 2001. Representing and Recognizing 
the Visual Appearance of Materials using Three-dimensional 
Textons. IJCV 43(1), pp. 29-44. 
Martin, D., Fowlkes, C. and Malik, J., 2004. Learning to detect 
natural image boundaries using local brightness, color, and tex- 
ture cues. IEEE TPAMI 26(5), pp. 530 —549. 
Mnih, V. and Hinton, G. E., 2010. Learning to detect roads in 
high-resolution aerial images. In: ECCV. 
Mnih, V. and Hinton, G. E., 2012. Learning to label aerial images 
from noisy data. In: ICML. 
Pal, M., 2005. Random forest classifier for remote sensing clas- 
sification. International Journal of Remote Sensing 26(1), 
pp. 217-222. 
Ranzato, M., Huang, E., Boureau, Y. and LeCun, Y., 2007. Un- 
supervised Learning of Invariant Feature Hierarchies with Ap- 
plications to Object Recognition. In: CVPR. 
Rezaei, Y., Mobasheri, M., Zoej, M. V. and Schaepman, M., 
2012. Endmember Extraction Using a Combination of Orthog- 
onal Projection and Genetic Algorithm. GRSL 9(2), pp. 161- 
165. 
Rottensteiner, F.,, Sohn, G., Jung, J., Gerke, M., Baillard, C., Ben- 
itez, S. and Breitkopf, U., 2012. The ISPRS benchmark on 
urban object classification and 3D building reconstruction. In: 
ISPRS Annals, Vol. I-3. 
Schapire, R. and Singer, Y., 1999. Improved boosting algorithms 
using confidence-rated predictions. Machine Learning 37(3), 
pp. 297—336. 
Schindler, K., 2012. An Overview and Comparison of Smooth 
Labeling Methods for Land-Cover Classification. IEEE TGRS 
50(11), pp. 4534-4545. 
Schmid, C., 2001. Constructing Models for Content-based Image 
Retrieval. In: CVPR. 
Schwartz, W., Kembhavi, A., Harwood, D. and Davis, L., 2009. 
Human detection using partial least squares analysis. In: 
ICCV. 
Shao, J. and Foerstner, W., 1994. Gabor wavelets for texture edge 
extraction. In: ISPRS Commission III Symposium. 
Tokarczyk, P., Montoya, J. and Schindler, K., 2012. An eval- 
uation of feature learning methods for high resolution image 
classification. ISPRS Annals. 
van Coillie, F., Verbeke, L. and Wulf, R. D., 2007. Feature se- 
lection by genetic algorithms in object-based classification of 
IKONOS imagery for forest mapping in Flanders, Belgium. 
Remote Sensing of Environment 110, pp. 476-487. 
Viola, P. and Jones, M., 2001. Rapid object detection using a 
boosted cascade of simple features. In: CVPR. 
Waske, B. and Benediktsson, J., 2007. Fusion of support vector 
machines for classification of multisensor data. IEEE TGRS 
45(12), pp. 3858-3866. 
Winn, A., Criminisi, A. and Minka, T., 2005. Object categoriza- 
tion by learned universal visual dictionary. In: ICCV. 
Zhu, S., Wu, Y. and Mumford, D., 1997. Minimax Entropy Prin- 
ciple and Its Application to Texture Modeling. Neural Compu- 
tation 9(8), pp. 1627-1660. 
      
   
   
  
  
  
  
  
  
  
  
   
   
  
    
   
  
    
   
   
   
     
  
  
   
  
  
   
   
    
    
   
    
   
  
  
    
   
  
  
   
   
   
     
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