Hyperspectral target detection with sparseness constraint

A sparseness constrained approach is proposed for linear unmixing, and the results are used for hybrid detection of hyperspectral imagery. The sparseness constraint is imposed on the abundance fractions, resulting in better performance than the popular non-negative and fully constrained methods, particularly in the situations when background endmember spectra are not accurately acquired or estimated, which is very common in practical applications. To increase the dictionary incoherence required for sparse regression, the use of band selection is proposed to improve the performance of sparseness constrained linear unmixing, thereby enhancing the following detection performance.