高光谱影像中波段数过多易导致“维数灾难”，而传统高光谱影像维数约简算法仅利用光谱特征而忽略了空间信息.针对上述问题，提出一种融合加权均值滤波与流形重构保持嵌入的维数约简算法.该方法利用影像中地物分布的空间一致性特点，对所有像素进行加权均值滤波，消除同类光谱差异性较大的像素影响，并在流形重构过程中增大空间近邻点的权重,提取出更为有效的鉴别特征，实现维数约简.在PaviaU和Urban高光谱数据集上的实验结果表明：相比于其它相关方法，该方法能获得更高的分类准确度，在分别随机选取5%和1%的训练样本情况下，其总体分类准确度分别提高到98.76%和80.21%.该方法在发现内在低维流形结构的同时，有效融入了影像中的空间信息，改善了分类性能.

Hyperspectral Image (HSI) contains a large number of spectral bands that easily results in the curse of dimensionality. The traditional classification methods just apply the spectral information while they ignore the spatial information. To address this problem, a dimensionality reduction algorithm combining Weighted Mean Filter (WMF) and Manifold Reconstruction Preserving Embedding (MRPE) was proposed in this paper. According to the spatial consistency property of HSI, firstly, the method applies WMF to all pixels which can reduce the spectral difference of data points from the same class. Then, the weights of the spatial neighbor points are enhanced in manifold reconstruction. This method effectively extracts the discriminant features and achieves the dimensionality reduction. Experimental results on PaviaU and Urban data sets show that the proposed method has better classification accuracy than other algorithms. When 5% and 1% of training samples were randomly selected from the two data sets, the overall accuracies based on MRPE can reach 98.76% and 80.21%. The proposed method enhances the low-dimensional manifold representation with the spatial information and improves the performance of HSI classification.