【摘要】 为了解决高光谱遥感影像的维数约简问题以提高分类算法的分类精度,并针对高光谱影像通常只包含少量标记样本的问题,提出了基于一种半监督邻域保持嵌入(SSNPE)和改进的KNN分类器的高光谱影像分类算法。该算法在NPE的基础上同时利用同类标记样本和邻域未标记样本获得数据的邻域嵌入结构,并且通过增加标记近邻样本的权重加大降维数据的鉴别性,进而增加k近邻分类器的样本分类精度。在Urban、Indian高光谱影像数据集上的实验结果表明,改进的算法的分类精度提高了约8.7%、3.6%以上,分类性能有了较明显的改善。更多还原

【Abstract】 In order to solve the dimension reduction problem of hyperspectral image to improve the classification algorithm’s classification accuracy rate and the problem that hyperspectral image usually contains little labeled samples,we proposed a hyperspectral image algorithm based on a semi-supervised neighborhood preserving embedding algorithm and improved k-Nearest Neighborhood classifier.This algorithm uses both the labeled samples and the unlabeled samples of the neighborhood based on Neighborhood Preserving Embedding to get the neighborhood embedding structure,and improve the classification feature through raising weight of the labeled neighboring samples,and thus improving the sample accuracy rate of KNN classifier.The experimental results on the Urban and Indian Pine data sets show that the accuracy rate of the proposed method is improved by more than about 8.7%,3.6%,respectively,and thus the classification performance has been improved clearly.更多还原