Abstract:
Aiming at the difficulty of labeling hyperspectral image samples and the problem that most feature extraction algorithms only consider spectral feature information but ignore spatial information, a new feature extraction of hyperspectral image with spatial-spectral collaboration-competition preserving graph embedding (SCPGE) in unsupervised scenes was proposed. The collaborative representation is used to characterize the global manifold structure, combined with locality-constrained property based on spatial information and spectral information to calculate the representation coefficient of the pixel. Used the weight matrix of the graph drawn by the coefficient matrix, and the objective function with regular term to obtain the projection matrix. The experimental results on Indian Pines and Salinas hyperspectral data sets show that the proposed method is better than others.