부산대학교 도서관

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.rse.2008.02.011 Byline: Jonathan Cheung-Wai Chan (a), Desire Paelinckx (b) Keywords: Ecotope mapping; Ensemble classification; Adaboost; Random Forest; Airborne hyperspectral; Band selection Abstract: Detailed land use/land cover classification at ecotope level is important for environmental evaluation. In this study, we investigate the possibility of using airborne hyperspectral imagery for the classification of ecotopes. In particular, we assess two tree-based ensemble classification algorithms: Adaboost and Random Forest, based on standard classification accuracy, training time and classification stability. Our results show that Adaboost and Random Forest attain almost the same overall accuracy (close to 70%) with less than 1% difference, and both outperform a neural network classifier (63.7%). Random Forest, however, is faster in training and more stable. Both ensemble classifiers are considered effective in dealing with hyperspectral data. Furthermore, two feature selection methods, the out-of-bag strategy and a wrapper approach feature subset selection using the best-first search method are applied. A majority of bands chosen by both methods concentrate between 1.4 and 1.8 [mu]m at the early shortwave infrared region. Our band subset analyses also include the 22 optimal bands between 0.4 and 2.5 [mu]m suggested in Thenkabail et al. [Thenkabail, P.S., Enclona, E.A., Ashton, M.S., and Van Der Meer, B. (2004). Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote Sensing of Environment, 91, 354-376.] due to similarity of the target classes. All of the three band subsets considered in this study work well with both classifiers as in most cases the overall accuracy dropped only by less than 1%. A subset of 53 bands is created by combining all feature subsets and comparing to using the entire set the overall accuracy is the same with Adaboost, and with Random Forest, a 0.2% improvement. The strategy to use a basket of band selection methods works better. Ecotopes belonging to the tree classes are in general classified better than the grass classes. Small adaptations of the classification scheme are recommended to improve the applicability of remote sensing method for detailed ecotope mapping. Author Affiliation: (a) Geography Department, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium (b) Research Institute for Nature and Forest (INBO), Kliniekstraat 25, 1070 Brussels, Belgium Article History: Received 1 March 2007; Revised 14 February 2008; Accepted 17 February 2008