Search

Sparse regression based on spectral libraries has become a promising alternative for addressing the hyperspectral unmixing problem. However, the actual endmembers of a scene are usually inconsistent with the corresponding spectral signatures in the spectral library, which largely limits the performance of sparse regression approaches. In this letter, a new sparse regression algorithm considering spectral library mismatch is proposed, which allows the spectral signatures in the spectral library to independently scale in each band and regularizes the differential of the scaling factors to be sparse. Moreover, a guided filter (GF)-based regularizer is introduced to explore the spatial-contextual information. The spectral library sparse scaling and GF constraints are combined to mitigate the impact of the spectral library mismatch. Experimental results on both synthetic and real data show that the proposed algorithm outperforms other methods that address the spectral library mismatch problem.

Sparse regression methods based on the spectral libraries have shown promise for hyperspectral unmixing, which assumes that the observed image can be represented linearly by a small subset of a known spectral library. However, the spectral mismatches are often assumed to occur between the actual endmembers and the corresponding signatures in the spectral library, which greatly limits the application of the sparse unmixing method. In this article, we address the abovementioned problem by adaptively adjusting the spectral library to match the current hyperspectral image during the unmixing procedure and propose a spectral library adaptive collaborative sparse unmixing model. In our model, we introduce both the spectral scaling factors and the spectral residual variables, which can adjust the spectral library in terms of spectral intensity and local spectral variations, respectively. Moreover, the first-order sparse property of the spectral difference is utilized to improve the accuracy of the spectral library adjustment. We solve our model by the alternating direction method of multipliers. Simulations and real-data experiments show that the proposed method is effective for reducing the impact of spectral library mismatches compared with state-of-the-art algorithms.

This paper provides an overview of attitude coordination problems of multi-rigid-body with the goal of promoting research in this area. Theoretical results regarding consensus seeking with different system models, different communication topologies, different control goals and different techniques are summarized. And applications of consensus protocols to multirigid- body coordination are investigated. Finally, some future research directions and open problems are also proposed.