Blind Unmixing of Hyperspectral Images Based on Online Dictionary Learning
In view of the unknown condition of the surface feature spectra in the hyperspectral images to be processed, with the introduction of an online dictionary learning into the hyperspectral sparse unmixing, this paper proposes a method of blind hyperspectral unmixing based on online dictionary learning and sparse coding.Atoms which are closest to the unknown surface feature spectra are selected from the training dictionary via the online dictionary learning and sparse coding statistics.They are used to estimate the surface feature spectra in the data to be processed.The simulation results show that the accurate extraction probability of this method is more than 66%, and the effective extraction probability is more than 89%.It can be used for reference in the research of spectral identification of ground objects.In addition, the training dictionary and sparse coding can be used to reconstruct the mixed pixel spectral vectors.Therefore, this method also works well in sparse unmixing.
NETL
NSTL
万方数据
维普
