Classification of Hyperspectral Images Based on Supervised Sparse Embedded Preserving Projection

Dimensionality reduction is an important research area for hyperspectral remote sensing images due to the redundancy of spectral information. Sparsity preserving projection (SPP) is a dimensionality reduction (DR) algorithm based on the l1-graph, which establishes the relations of samples by sparse representation. However, SPP is an unsupervised algorithm that ignores the label information of samples and the objective function of SPP; instead, it only considers the reconstruction error, which means that the classification effect is constrained. In order to solve this problem, this paper proposes a dimensionality reduction algorithm called the supervised sparse embedded preserving projection (SSEPP) algorithm. SSEPP considers the manifold structure information of samples and makes full use of the label information available in order to enhance the discriminative ability of the projection subspace. While maintaining the sparse reconstruction error, the algorithm also minimizes the error between samples of the same class. Experiments were performed on an Indian Pines hyperspectral dataset and HJ1A-HSI remote sensing images from the Zhangjiang estuary in Southeastern China, respectively. The results show that the proposed method effectively improves its classification accuracy.