Random forests of binary hierarchical classifiers for analysis of hyperspectral data

@inproceedings{Crawford2003_randomforest_hyperspectral, abstract = {Statistical classification of hyperspectral data is challenging because the input space is high in dimension and correlated, but labeled information to characterize the class distributions is typically sparse. The resulting classifiers are often unstable and have poor generalization. A new approach that is based on the concept of random forests of classifiers and implemented within a multiclassifier system arranged as a binary hierarchy is proposed. The primary goal is to achieve improved generalization of the classifier in analysis of hyperspectral data, particularly when the quantity of training data is limited. The new classifier incorporates bagging of training samples and adaptive random subspace feature selection with the binary hierarchical classifier (BHC), such that the number of features that is selected at each node of the tree is dependent on the quantity of associated training data. Classification results from experiments on data acquired by the Hyperion sensor on the NASA EO-1 satellite over the Okavango Delta of Botswana are superior to those from our original best basis BHC algorithm, a random subspace extension of the BHC, and a random forest implementation using the CART classifier.}, author = {Crawford, M. M. and Ham, Jisoo and Chen, Yangchi and Ghosh, J. }, citeulike-article-id = {784838}, journal = {IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data}, keywords = {ensemble, machine-learning, randomforest, remote-sensing}, pages = {337--345}, posted-at = {2006-08-03 17:51:30}, priority = {2}, title = {Random forests of binary hierarchical classifiers for analysis of hyperspectral data}, url = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=1295213}, year = {2003} }

TY - CONF ID - Crawford2003_randomforest_hyperspectral L3 - citeulike-article-id:784838 TI - Random forests of binary hierarchical classifiers for analysis of hyperspectral data JF - IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data SP - 337 EP - 345 N2 - Statistical classification of hyperspectral data is challenging because the input space is high in dimension and correlated, but labeled information to characterize the class distributions is typically sparse. The resulting classifiers are often unstable and have poor generalization. A new approach that is based on the concept of random forests of classifiers and implemented within a multiclassifier system arranged as a binary hierarchy is proposed. The primary goal is to achieve improved generalization of the classifier in analysis of hyperspectral data, particularly when the quantity of training data is limited. The new classifier incorporates bagging of training samples and adaptive random subspace feature selection with the binary hierarchical classifier (BHC), such that the number of features that is selected at each node of the tree is dependent on the quantity of associated training data. Classification results from experiments on data acquired by the Hyperion sensor on the NASA EO-1 satellite over the Okavango Delta of Botswana are superior to those from our original best basis BHC algorithm, a random subspace extension of the BHC, and a random forest implementation using the CART classifier. KW - ensemble KW - machine-learning KW - randomforest KW - remote-sensing AU - Crawford, MM AU - Ham, Jisoo AU - Chen, Yangchi AU - Ghosh, J PY - 2003/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1295213 ER -