Hyperspectral Unmixing Algorithm Study Based On Endmember Subset Selection

For mixed pixel decomposition error presents,an hyperspectral unmixing optimization algorithm based on endmember subset selection.Through maximization volume simplex algorithm extracts endmembers.Because endmembers subset had similar endmembers and similar endmembers had an impact on the accuracy of spectral unmixing,spectral information divergence and spectral gradient angle is used for spectral discrimination to remove similar endmembers.By sorting the resulting endmember,followed by additional endmembers,endmembers meet the criteria will add into endmember groups and the resulting optimized endmembers will achieves.This method effectively removes interference of similar end,and no long er need to search combinations of endmembers.Each endmembers corresponding to the importance of the number of mixed will use in nonrestricted least squares calculation,and more precise subset of hyperspectral endmember wil achieve.Ef-ficiency and reliability of hyperspectral unmixing optimization algorithm will improve.The depth of interpretation method on hyperspectral remote sensing image is of very great significance.