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Sparse unmixing of hyperspectral data is an important technique which aims at estimating the fractional abundances of endmembers (pure spectral components). It is well known that enforcing sparseness becomes a necessary process in sparse unmixing methods. To better exploit the sparsity in hyperspectral imagery, a double reweighted sparse unmixing algorithm has been proposed. However, it focusses on...
For the purpose of filtering road spectrum this paper proposed an adaptive Kalman filtering (AKF) algorithm based on accurate autoregressive (AR) model. The proposed AKF can overcome the disadvantages such as complicated computation process and poor real-time performance when applying normal AKF. The algorithm can implement AKF by adjusting gain matrix Kk on the base of innovation. The process of...
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