Collaborative sparse unmixing of hyperspectral data using L2,P norm

Sparse unmixing is a popular method in remotely sensed hyperspectral imagery interpretation. Recently, the collaborative sparse unmixing model has shown its advantage over the traditional single channel sparse unmixing method since it can utilize the subspace nature of the high-dimensional hyperspectral data to alleviate the difficulty caused by the usually high mutual coherence of the spectral library. However, the existing collaborative sparse unmixing model is constructed in the convex ℓ
₁
norm framework while it is known that using the ℓ
_p
(0 <; p <; 1) norm can find a sparser solution. In this paper, we propose a collaborative sparse unmixing model and a new algorithm based on the ℓ
_2;p
(0 <; p <; 1) norm. Experimental results on both synthetic and real data demonstrate the effectiveness of the ℓ
_2;p
(0 <; p <; 1) norm collaborative sparse unmixing model.