The bilinear mixing model is a more realistic, generalized model that can represent a wide range of real-world hyperspectral images with tolerable accuracy. The use of a spectral library makes the problem more tractable. However, the high mutual coherence of the spectral library creates computational as well as performance issues in library-aided bilinear unmixing. Besides, the high mutual coherence of the spectral library reduces the accuracy of these unmixing methods, and the high cardinality of the spectral library increases the computational complexity. We propose a computationally efficient, two-phase library pruning approach for unmixing hyperspectral image, which also withstands a highly coherent spectral library. In this work, we first segregate the data into pixels generated due to linear and bilinear interaction using the subspace clustering method and subsequent rank estimation strategy. We subsequently reduce the mutual coherence of the spectral library and prune the linear interactions. In the next stage, we create a library corresponding to the bilinear components assuming that only the secondary reflections of the pruned library elements may be prevalent in these pixels. We perform pruning using a novel, low-rank based, sequential approach. Finally, we compute the abundance of the matrix by exploiting sparseness of the abundance matrix and include its low-rankness, and spatial structural similarity as regularization. We validate the overall advantages of our proposed framework on several real and synthetic data experiments.
You have requested a machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Neither SPIE nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations.
Translations are not retained in our system. Your use of this feature and the translations is subject to all use restrictions contained in the Terms and Conditions of Use of the SPIE website.