面向宏观地表分类的特征选择算法比较研究

为了成功将土地覆盖进行分类，选择合适的特征是至关重要的。针对利用MODIS数据进行宏观土地覆盖的分类问题，对三种典型的特征选择方法进行了比较研究。研究结果表明：分支定界法（BB）最适合于该土地覆盖分类问题，与此同时，ReliefF和mRMR方法在目标应用中的精度非常接近。研究结果同样表明进行特征选择是非常必要的，它不仅能够大大地降低计算复杂度，而且分类精度能够保持不变，甚至更高。

Comparative study of feature selection methods for regional land cover classification

Selecting suitable features is very crucial for achieving successful classification of land cover types.This paper presents a comparative study of three typical feature selection methods for the task of regional land cover classification using MODIS data.Comparison results have shown that Branch and Bound is the best for land cover classification with MODIS data,while ReliefF and mRMR achieve nearly the same accuracies on the target application.The experimental results also show that it is necessary to conduct feature selection,which can reduce the computation cost largely,while the accuracy remains the same or even better.