Hyperspectral image classification with partial least square forest
Hyperspectral image classification with partial least square forest
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DOI：
10.1109/igarss.2017.8127790
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发表时间：
2017-07
期刊：
影响因子：
--
通讯作者：
J. Xia;N. Yokoya;A. Iwasaki
中科院分区：
文献类型：
--
作者：
J. Xia;N. Yokoya;A. Iwasaki
在高光谱遥感领域，决策森林结合了多个决策树(DTD)的预测以获得更好的预测性能。随机森林(RF)和轮换森林(ROF)是两种广为人知且功能强大的决策森林。提出了一种新的决策森林--偏最小二乘森林(PLSF)。在PLSF中，我们采用偏最小二乘法来获得用于超平面分裂的分量。此外，使用投影自举技术来保留整个光谱波段，以便在投影空间中选择分裂。在三个高光谱数据集上的实验结果表明，与RF和ROF相比，所提出的PLSF算法在集成范围内提高了分集和精度。
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.