International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
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REFERENCES
Arefi, H., Hahn, M., and Lindenberger, J., 2003. Lidar data
Iterative Feature Selection classification with remote sensing tools, Proceedings of the
0.08 ISPRS Commission IV Joint Workshop: Challenges in
> : Geospatial Analysis, Integration and Visualization II, 08-09
& 0.07 September, Stuttgart, Germany, pp. 131-136.
>
2 0.06 Breiman, L., Friedman, H., Olshen, A. and Stone, J., 1984.
o 0.05 Classification and Regression Trees. Monterey, CA: Wadsworth
= & Brooks/Cole Advanced Books & Software.
9 0.04
9 0.03 Breiman, L., 1996. Bagging predictors. Machine Learning, 24
a
= (2), 123-140.
a 0.02
z 0.01 Breiman, L., 2001. Random Forests. Machine Learning, 45, 5—
32.
48 .39 | 36... 30 26. 221.15
Feature Numbers Chehata, N., Guo, L. and Mallet, C., 2009. Airborne lidar
Figure 4. Iterative feature selection
4. CONCLUSIONS
In this study Random Forests is successfully applied to the
feature selection for land-use classification. There are 48
features extracted from lidar data and imagery. Making use of
the Random Forests, an assembling classification tree, that
provides feature importance index, we iteratively eliminate
features with less important index until the mean decrease
accuracy is stable. The extensive experiments are conducted to
describe the Random Forests' characteristics and prove its
performance. Classification results suggest that much more
feature cannot guarantee the improvement of classification
accuracy, and confirms that the selected features can obtain the
satisfied classification results. Overall, the classification results
indicate that the selected features agree the existing
physiological knowledge.
feature selection for urban classification using random forests.
In: F. Bretar, M. Pierror-Deseilligny & G. Vosselman, eds. Laser
Scanning, IAPRS, 38(3/W8). s.l.:s.n.
Congalton, G., 1991. A review of assessing the accuracy of
classifications of remotely sensed data, Remote Sensing of
Environment, 37(1): 35-46.
Congalton, G. and Green, K., 2009. Assessing the Accuracy of
Remotely Sensed Data: Principles and Practices, Taylor &
Francis, Boca Raton, FL. pp. 85-89.
Diaz-Uriarte, R., and Alvarez de Andres, S. 2006. Gene
selection and classification of microarray data using random
forest, BMC Bioinformatics, 7(3).
Gislason, P. O., Benediktsson, J. A. and Sveinsson, J. R., 2006.
Random Forests for land cover classification, Pattern
Recognition letters, 27:294-300.
Guo, L. Chehata, N., Mallet, C. and Boukir, S., 2011.