Multilayer Unmixing for Hyperspectral Imagery With Fast Kernel Archetypal Analysis

The multilayer network in deep learning provides a promising means for rich data representation. Inspired by this approach, we investigate multilayer unmixing for spectral decomposition with fast kernel archetypal analysis (KAA). KAA is used for endmember extraction and abundance estimation simultaneously. To refine the initial unmixing results, a multilayer process is utilized to provide final unmixing results at the end of the network. Moreover, a fast implementation of KAA is proposed via using the Nystrom method to relieve KAA's memory issue and decrease the processing time. The proposed method is tested on both synthetic and real hyperspectral image data sets. The results demonstrate that the multilayer unmixing algorithm outperforms the conventional unmixing techniques.