Title :
Random forest classifiers for hyperspectral data
Author :
Joelsson, Sveinn R. ; Benediktsson, Jon Atli ; Sveinsson, Johannes R.
Author_Institution :
Dept. of Electr. & Comput. Eng., Iceland Univ., Reykjavik, Iceland
Abstract :
Two random forest (RF) approaches are explored; the RF-BHC (binary hierarchical classifier) and the RF-CART (classification and regression tree). Both methods are based on a collection (forest) of tree-like classifier systems where the difference is in the way the trees are grown. The BHC approach depends on class separability measures and the Fisher projection, which maximizes the Fisher discriminant where each tree is a class hierarchy, and the number of leaves is the same as the number of classes. The CART approach is based on CART-like trees where trees are grown to minimize an impurity measure. Here, these different RF approaches are compared in experiments. The RF approaches were investigated in experiments by classification of an urban area from Pavia, Italy using hyperspectral ROSIS (reflective optics system imaging spectrometer) data provided by DLR.
Keywords :
geophysical signal processing; geophysical techniques; image classification; multidimensional signal processing; regression analysis; remote sensing; signal classification; spectral analysis; trees (mathematics); Fisher discriminant; Fisher projection; Italy; Pavia; binary hierarchical classifier; class separability measure; classification tree; hyperspectral ROSIS data; hyperspectral data; impurity measure; random forest classifiers; reflective optics system imaging spectrometer; regression tree; Bagging; Boosting; Classification tree analysis; Hyperspectral imaging; Hyperspectral sensors; Impurities; Radio frequency; Regression tree analysis; Urban areas; Voting;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2005. IGARSS '05. Proceedings. 2005 IEEE International
Print_ISBN :
0-7803-9050-4
DOI :
10.1109/IGARSS.2005.1526129