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Endmember extraction (EE) is one of the most important issues in hyperspectral mixture analysis, and it is also one of the most challenging tasks due to the intrinsic complexity of remote sensing images and the lack of priori knowledge. In recent years, a number of EE methods have been developed, and several different optimization objectives have been proposed from different perspectives. In all of...
Spectral unmixing is one of the most important techniques for analyzing hyperspectral images and many hyperspectral unmixing algorithms were developed under an assumption that pure pixels exist in recent years. However, the pure-pixel assumption may be seriously violated for highly mixed data. Endmember extraction can be regards as an optimization problem no matter whether pure-pixel exists or not...
In this paper, endmember extraction algorithm is described as a combinatorial optimization problem. A novel quantum-behaved particle swarm optimization (QPSO) approach which employs quantum-behaved particle swarm optimization to find endmembers with good performance is proposed. As far as our knowledge, it is the first time that quantum-behaved particle swarm optimization is introduced into hyperspectral...
Spectral unmixing is an important technique for hyperspectral data interpretation, in which a mixed spectral signature is decomposed into a collection of spectrally constituent and pure spectra, called endmembers, and a set of correspondent fractions, or abundances, that indicate the proportion of each endmember's presence in the mixture. As is known to all, we can get abundances with given endmembers...
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