中文摘要：

为进一步提高邻域保持嵌入算法（NPE）在高光谱影像分类中的识别性能，提出一种改进的半监督邻域保持嵌入（SSNPE）算法。首先，该算法在NPE算法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。然后，通过增加近邻标记样本的权重加大降维数据的鉴别性。最后，通过利用k近邻分类器（KNN）对样本进行分类得到该算法在数据集上的分类性能。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相比其他算法提高了约8.3%、6.2%以上，分类性能上有了较为明显的提高。

英文摘要：

Neighborhood Preserving Embedding （NPE） algorithm is a sub-space learning method, which has the ability to preserve the local neighboring structure information of the date. In order to improve the recognition function of the NPE algorithm used in hyperspectral image classification, we proposed an improved Semi-supervised Neighborhood Preserving Embedding （SSNPE） algorithm. Firstly, the algorithm uses both the labeled samples and the unlabeled samples of the neighborhood to get the neighborhood embedding structure. Secondly, improve the classification feature of the samples through raising weight of the labeled neighboring samples. Finally, get the classification function through using k-nearest Neighboring （KNN） classifier to classify the data set. The experimental results on the Urban, Indian Pine data sets show that the classification rate of the proposed algorithm is improved by more than about 8.3%, 6.2% compared to other algorithms, respectively, and thus the recognition performance has been improved clearly.