面向高光谱分类的局部几何稀疏保持嵌入EI北大核心CSCD

大量维数约简(Dimensionality reducion, DR)方法表明保持数据间稀疏特性的同时,确保几何结构的保持能更有效提取出具有鉴别性的特征,为此本文提出一种联合局部几何近邻结构和局部稀疏流形的维数约简方法.该方法首先通过局部线性嵌入方法重构每个样本以保持数据的局部线性关系,同时计算样本邻域内的局部稀疏流形结构,在此基础上通过图嵌入框架保持数据的局部几何近邻结构和稀疏结构,最后在低维嵌入空间中使类内数据尽可能聚集,提取低维鉴别特征,从而提升地物分类性能.在Indian Pines和PaviaU高光谱数据集上的实验结果表明,本文方法相较于传统维数约简方法能明显提高地物的分类性能,总体分类可达到83.02%和91.20%,有利于实际应用.

Local Geometry and Sparsity Preserving Embedding for Hyperspectral Image Classification

A large number of dimensionality reduction methods show that while maintaining the sparse characteristics between data, ensuring that the geometry is maintained can more effectively extract discriminative features. To address this issue, a dimensionality reduction (DR) method combining joint local geometry neighbor structure and local sparse manifold is proposed. The method first reconstructs each sample by local linear embedding to maintain the local linear relationship, and calculates the local sparse manifold structure in the neighbors. Then the local geometry neighbor structure and sparse manifold structure are maintained by the graph embedding frame. Finally, in the low-dimensional embedded space, the intra-class data is compacted as much as possible, so that the low-dimensional discriminative features are extracted to improve the classification performance. The experimental results on the Indian Pines and PaviaU hyperspectral datasets show that the proposed method can significantly improve the classification precision compared with the traditional DR methods. The overall classification can reach 83.02% and 91.20%, respectively, which is beneficial to practical applications.