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Spectral unmixing is one of the important techniques for hyperspectral data processing. The analysis of spectral mixing is often based on a linear, fully constrained (FC) (i.e., nonnegative and sum-to-one mixture proportions), and least squares criterion. However, the traditional iterative processing of FC least squares (FCLS) linear spectral mixture(More)
Spectral unmixing is an important technique of hyperspectral imagery processing. The traditional iterative processing of least squares linear spectral mixture analysis is of heavy computational burden. In this paper, a simple distance measure is proposed based on support vector machine (SVM). The method is free of iteration and dimensionality reduction,(More)
Iterative Error Analysis (IEA) widely known as a good endmember extraction (EE) algorithm. It is robust, automatic and free of data transformation. However, IEA is faced with risks in some cases due to the sole use of unmxing distance, and its speed is lowed down by the iteration-based linear spectral mixture analysis (LSMA). To make IEA algorithm faster(More)
A new Hyperspectral image band selection algorithm based on maximal standard deviation is proposed to reduce spectral redundancy of Hyperspectral remote sensing image and computational complexity. It first uses standard deviation to measure the band information. The correlation between band and selecting band is then used as a weight factor for standard(More)
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