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Investigation of the random forest framework for classification of hyperspectral data
2005
IEEE Transactions on Geoscience and Remote Sensing
Statistical classification of byperspectral data is challenging because the inputs are high in dimension and represent multiple classes that are sometimes quite mixed, while the amount and quality of ground truth in the form of labeled data is typically limited. The resulting classifiers are often unstable and have poor generalization. This paper investigates two approaches based on the concept of random forests of classifiers implemented within a binary hierarchical multiclassifier system,
doi:10.1109/tgrs.2004.842481
fatcat:t6gxpls2srfxfmmhzm5yuv7lda