Spatial preprocessing for spectral endmember extraction by local linear embedding

Endmember extraction (EE) has been widely utilized to identify spectrally unique signatures of pure ground materials in hyperspectral images. Most of existing EE algorithms focus on spectral signature only, denoted as spectral EE (sEE) algorithms in this paper. In order to improve the performance of these sEE algorithms by considering spatial information, a novel spatial preprocessing (SPP) strategy based on Locally Linear Embedding (LLE) is proposed to alleviate the influence of spectral variation. Specifically, the LLE is adopted to revise pixels by smoothing spectral variation in their spatial neighborhood. Furthermore, anomalous pixels, which may be smoothed excessively by many current SPP algorithms, can be well retained by tuning off the spatial preprocessing if their signatures are revised unexpectively. As a result, the anomalous endmembers can be correctly identified by the proposed LLE based SPP algorithm. Experimental results on simulated benchmark dataset have demonstrated that the proposed LLE based SPP algorithm outperforms many state-of-the-art SPP algorithms.