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提出了一种基于粗集属性划分的遥感分类新方法,构造了基于粗集的集成遥感分类器。该分类器利用粗集理论将输入的属性集合划分为多个约减,利用这些约减构造多个训练子集。每个训练子集训练神经网分类器,在决策时将多个单个分类器的结果进行投票选举。这种方法即减少了单个分类器的输入属性个数,又避免了由于属性选取造成单一分类器在某些分类上的错误偏见。该分类器与神经网分类器方法,以及属性选取与神经网结合方法进行了比较。结果表明RSEC无论在分类精度上,还是在不同样本个数条件下的精度稳定程度上均有较好表现。 更多还原

【Abstract】 Supervised classification in remote sensing imagery is receiving increasing attention in current research. In order to improve the classification ability,a lot of spatial-features (e.g.,texture information generated by GLCM) have been utilized. Unfortunately,too many features often cause classifier over-fit to a certain features’ character and lead to lower classification accuracy. The traditional feature selection algorithms have an unstable classification performance which depends on the numbe... 更多还原