Hyperspectral Unmixing via Latent Multiheterogeneous Subspace

Blind hyperspectral unmixing (BHU) is an important technology to decompose the mixed hyperspectral image (HSI), which is actually an ill-posed problem. The ill-posedness of the BHU is deteriorated by nonlinearity, endmember variability (EV) and abnormal points, which are considered as three challenging intractable interferences currently. To sidestep the challenges, we present a novel unmixing model, where a latent multidiscriminative subspace is explored and the inherent self-expressiveness property is employed. The most existing unmixing approaches directly decompose the HSI utilizing original features in an interference corrupted single subspace, unlike them, our model seeks the underlying intrinsic representation and simultaneously reconstructs HSI based on the learned latent subspace. With the help of both clustering homogeneity and intrinsic features selection, structural differences in the HSI and the spectral property of a certain material are exploited perfectly, and an ideal multiheterogeneous subspace is recovered from the heavily contaminated original HSI. Based on the multiheterogeneous subspace, the reconstructed differentiated transition matrix is split into two matrices to avoid the emergence of the artificial endmember. Experiments are conducted on synthetic and four representative real HSI sets, and all the experimental results demonstrate the validity and superiority of our proposed method.