为了充分利用高光谱图像蕴含的丰富的光谱信息和空间信息, 提出了结合多尺度空间滤波和层级网络的基于结构保持的高光谱特征选择算法.算法利用基于l2,1范数的数学模型, 选出同时保存全局相似性结构和局部流形结构的特征子集; 在多个尺度的窗口中使用双边滤波, 自适应计算滤波核, 自动在光谱数据中融入空间信息, 增强了类内相似性和类间相异性, 避免了参量选择; 引入层级结构实现空间信息和光谱信息的深入融合, 提高了分类准确度; 讨论了层级数目和窗口尺度个数对分类准确度的影响.在Indian Pines和PaviaU两个数据集的实验表明, 该算法在大部分地物种类上的分类准确度都有较大幅度的提升, 总体分类准确度分别达到90.98%和94.20%, 相比其他方法明显提高了地物分类准确度.

In order to make full use of the abundant spectral and spatial information of hyperspectral images, a novel feature selection algorithm based on the structure preserving combination with the multi-scale spatial filtering and the hierarchical network is proposed. The feature subset that best preserving the global similarity and the local manifold structure is selected via l2,1 norm mathematical model. The bilateral filtering with multi-scale window and adaptive parameter setting is used for incorporating spatial information into spectral data automatically, enhancing the similarity within class and dissimilarity between different classes. The hierarchical network is introduced to achieve further integration of spatial and spectral information that benefit the classification. The influence of the hierarchical network depth and spatial filtering scale number is analyzed. The experiments validate the effectiveness of the algorithm. The overall classification accuracies reaches to 90.98% and 94.20% on Indian Pines and PaviaU data sets respectively, which significantly improve the classification of land cover compared with conventional methods.