Sparse regression based unmixing has been recently proposed to estimate the abundance of materials present in hyperspectral image pixel. In this paper, a novel sparse unmixing optimization model based on approximate sparsity, namely, approximate sparse unmixing (ASU), is firstly proposed to perform the unmixing task for hyperspectral remote sensing imagery. And then, a variable splitting and augmented Lagrangian algorithm is introduced to tackle the optimization problem. In ASU, approximate sparsity is used as a regularizer for sparse unmixing, which is sparser than l1 regularizer and much easier to be solved than l0 regularizer. Three simulated and one real hyperspectral images were used to evaluate the performance of the proposed algorithm in comparison to l1 regularizer. Experimental results demonstrate that the proposed algorithm is more effective and accurate for hyperspectral unmixing than state-of-the-art l1 regularizer.

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sparse unmixing optimization model approximate sparsity namely approximate sparse unmixing asu variable splitting and augmented lagrangian algorithm real hyperspectral images hyperspectral remote sensing l0 simulated stateoftheart l1 regularizer regression based unmixing estimate the abundance of materials present in hyperspectral image

Chengzhi Deng,Yaning Zhang,Shengqian Wang,Shaoquan Zhang,Wei Tian,Zhaoming Wu,Saifeng Hu,.Approximate Sparse Regularized Hyperspectral Unmixing. 2014 (),.

Chengzhi Deng,Yaning Zhang,Shengqian Wang,Shaoquan Zhang,Wei Tian,Zhaoming Wu,Saifeng Hu,"Approximate Sparse Regularized Hyperspectral Unmixing" 2014

Chengzhi Deng,Yaning Zhang,Shengqian Wang,Shaoquan Zhang,Wei Tian,Zhaoming Wu,Saifeng Hu,"Approximate Sparse Regularized Hyperspectral Unmixing"