基于稀疏性的高光谱解混是近年来高光谱混合像元分解的研究热点。主要研究了L₁正则化的高光谱混合像元分解算法。首先分析了L₁正则化的三种解混模型，即无约束、非负约束和全约束模型；然后给出了三种模型对应的数值求解算法；最后，采用模拟的和真实的高光谱数据进行实验，比较了三种高光谱混合像元分解算法的效果。实验结果表明：三种模型均具有很好的高光谱混合像元分解精度(SRE)，其中全约束模型最好，非负约束模型次之，无约束模型最差；全约束模型在信噪比低和端元数多的情况下，仍然获得较高的SRE。

Hyperspectral unmixing based on sparsity is a research hotspot in recent years. This paper studies the hyperspectral unmixing algorithms based on L₁ regularization. First we analyzed three unmixing models, including unconstrained model, non-negative constraint model and full-constrained model. And then the corresponding algorithms are presented. In the end, both simulated and real hyperspectral data sets are used to compare and evaluate the proposed three hyperspectral unmixing algorithms. Experimental results demonstrate that three models all have good high-precision. The full constrained model achieves the best unmixing precision(SRE). The non-negative constrained model is better. And the unconstrained model is worst. In particular, the fully constrained model achieves the higher SRE under the low signal to noise ratio and a large amount of endmembers situation.