The algorithm proposed here not only imposes an uncorrelated constraint to reduce data redundancy, but also utilizes the class information and the interclass separability after projection is enhanced.
本文算法使得所提特征之间相互无关，这样降低了数据冗余，同时考虑到类别信息，使得投影后的类间区分度加强了。
Firstly to select the bands that have class separability by K2 algorithm, then remove the redundant bands based on conditional mutual information test.
使用K2结构学习算法选出具有类别可分性的波段，进一步利用互信息测试对遥感波段之间的相关性做分析，去除冗余信息。
Firstly to select the bands that have class separability by K2 algorithm, then remove the redundant bands based on conditional mutual information test.
使用K2结构学习算法选出具有类别可分性的波段，进一步利用互信息测试对遥感波段之间的相关性做分析，去除冗余信息。
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