A Hyperspectral Imagery Unmixing Method Based on Abundance Division
Hyperspectral remote sensing imagery provides rich spectral information and space information about the place of interest. Mixed pixels happen in spectral images frequently, which reduce the classification accuracy of ground truth. Due to spectral variability, early spectral unmixing methods using one pixel as endmember ectral unmixing can't provide enough performance for applications. Pays more attention to the multiple endmember spectral analysis, and proposes an unmixing method based on abundance division. Experiments on real hyper-spectal images show high performance.
NETL
NSTL
万方数据
维普
