为了提高高光谱图像的分类精度, 有效利用高光谱图像的空间信息和光谱信息对高光谱图像进行预处理, 本文提出了一种新的空谱联合特征提取方法, 加权空-谱局部保持投影算法(WSSLPP)。该算法结合高光谱图像的物理特性对高光谱图像进行重构, 降低了图像中奇异点的干扰; 然后对局部像素近邻保持嵌入(LPNPE)和局部保持投影(LPP)的目标函数进行加权求和, 有效融合高光谱图像空间维和光谱维的信息来构建投影矩阵。WSSLPP不仅保留了高光谱图像在空间维上像素间的近邻关系, 而且保持了在光谱维上样本的固有结构, 有利于高光谱图像的分类。在Indian Pines和PaviU数据库上对该算法进行验证分析, 结果表明: 基于WSSLPP算法得到的分类精度明显高于其他算法, 总体分类精度的最大值分别为99.00%, 99.50%, 有效提高了高光谱图像的分类精度。

In order to improve the classification accuracy of Hyperspectral images (HSI) and preprocess HSI by effectively using the spatial and spectral information of HIS, a new spatial-spectral feature extraction method, Weighted Spatial and Spectral Locality Preserving Projection (WSSLPP) is proposed in this paper. The HSI was reconstructed combining the physical characters of HSI to avoid the interference of singular point; then the target functions of locality pixel neighbor preserving embedding (LPNPE) and locality preserving projection (LPP) were weighted and summed, thus the spatial and spectral dimension information of HSI was effectively fused to construct the projection matrix. WSSLPP not only keeps the pixel neighborhood in spatial domain, but also keeps the implicit structure of samples in spectral domain, which helps for the HIS classification. The benchmark verification on Indian Pines and PaviU database show that the classification accuracy resulted from WSSLPP algorithm is significantly higher than that from other algorithms, and the overall classification accuracy is 99.00% and 99.50% respectively, effectively improving the HSI classification accuracy.