Adaptive endmember extraction based sparse nonnegative matrix factorization with spatial local information

Hyperspectral Unmixing aims at getting the endmember signature and their corresponding abundance maps from highly mixed Hyperspectral image. Nonnegative Matrix Factorization (NMF) is a widely used method for spectral unmixing because it can obtain better performance while there is no pure pixels in the hyperspectral image. However, many methods based on nonnegative matrix factorization seldom consider the spatial information both on local and nonlocal. To combine the spatial and spectral information together to improve the unmixing accuracy, an adaptive endmember extraction based sparse nonnegative matrix factorization with spatial local information (ASNMF) is proposed in this paper. A superpixel segmentation is to obtain many meaningful regions which are spectral similar and spatial adjacent. Endmember is adaptively extracted on each superpixel to generate endmember set. Initialing the endmember set, ASNMF could adaptively obtain the final endmembers with the sparse nonnegative matrix factorization. Both the experiments on synthetic and real scene images show the effectiveness of the proposed method for hyperspectral unmixing.