A novel blind spectral Unmixing method based on error analysis of linear mixture model

It is well known that the linear mixture model (LMM) is attracting much attention due to its simplicity. However, some theoretical analysis reveals that the traditional LMM also impedes the improvement of blind spectral unmixing. For this reason, we propose a novel blind spectral unmixing method (NBSUM) in this letter. NBSUM utilizes the conjugate gradient to calculate end-member spectral and abundance, which can not only overcome some shortcomings of the traditional LMM but also provide more accurate results. NBSUM is compared with some state-of-the-art approaches on both synthetic and real hyperspectral data sets, and the experimental results demonstrate the efficacy of the proposed method.