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Linear spectral mixture analysis (LSMA) has been widely used in remote sensing community for spectral unmixing. This letter develops a promising technique, called kernel-based LSMA (KLSMA), which uses nonlinear kernels to resolve the issue of nonlinear separability arising in unmixing and further extends several commonly used LSMA techniques to their kernel-based counterparts. Interestingly, according to experiments conducted for real hyperspectral and multispectral images, KLSMA is more effective than LSMA when data samples are heavily mixed.
Date of Publication: Jan. 2012