On the detection of linear mixtures in hyperspectral images
In order to provide reliable information on the instantaneous field-of-view considered in hyperspectral images through spectral unmixing, understanding the kind of mixture that occurs over each pixel plays a crucial role. In this paper, a new method for fast detection of linear mixtures is introduced. The proposed method does not need statistical information and performs an a priori test on the spectral linearity of each pixel. It uses standard least squares optimization to achieve estimates of the likelihood of occurrence of linear combinations of endmembers by taking advantage of geometrical properties of hyperspectral signatures. Experimental results on synthetic datasets show how the aforesaid algorithm is actually able to deliver a reliable and thorough assessment of the kind of mix on the scene.