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. © 2022 Science Press. All rights reserved.