Hyperspectral image classification with partial least square forest

In the hyperspectral remote sensing community, decision forests combine the predictions of multiple decision trees (DTs) to achieve better prediction performance. Two well-known and powerful decision forests are Random Forest (RF) and Rotation Forest (RoF). In this work, a novel decision forest, called Partial Least Square Forest (PLSF), is proposed. In the PLSF, we adapt PLS to obtain the components for the hyperplane splitting. Moreover, the projection bootstrap technique is used to retain the full spectral bands for the selection of split in the projected space. Experimental results on three hyperspectral datasets indicated the effectiveness of the proposed PLSF because it enhances the diversity and accuracy within the ensemble when compared to RF and RoF.

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