Nonlocal Tensor-Based Sparse Hyperspectral Unmixing

Sparse unmixing is an important technique for analyzing and processing hyperspectral images (HSIs). Simultaneously exploiting spatial correlation and sparsity improves substantially abundance estimation accuracy. In this article, we propose to exploit nonlocal spatial information in the HSI for the sparse unmixing problem. Specifically, we first group similar patches in the HSI, and then unmix each group by imposing simultaneous a low-rank constraint and joint sparsity in the corresponding third-order abundance tensor. To this end, we build an unmixing model with a mixed regularization term consisting of the sum of the weighted tensor trace norm and the weighted tensor l(2,1)-norm of the abundance tensor. The proposed model is solved under the alternating direction method of multipliers framework. We term the developed algorithm as the nonlocal tensor-based sparse unmixing algorithm. The effectiveness of the proposed algorithm is illustrated in experiments with both simulated and real hyperspectral data sets.

Automated summary

Sparse unmixing is an important technique for analyzing hyperspectral images, and this article proposes a nonlocal tensor-based sparse unmixing algorithm that groups similar patches in the image and applies low-rank constraint and joint sparsity for accurate abundance estimation. The effectiveness of the algorithm is demonstrated through experiments with simulated and real hyperspectral data sets.