一种新的空谱联合稀疏高光谱目标检测方法

目标检测是高光谱数据处理的重要应用之一，高光谱图像中空间和光谱信息的充分利用对于目标检测率的有效提升非常关键。提出一种新的联合稀疏表示的目标检测方法，将混合范数理论和算法应用于高光谱目标检测，在联合高光谱图像空间和光谱信息的基础上，建立了基于联合稀疏性约束的混合范数正则化数学模型，并利用交替方向乘子法对模型进行了优化求解。仿真实验结果表明，该方法能有效提高高光谱目标检测的准确性，降低虚警率。

A Novel Spectral-spatial Sparse Method for Hyperspectral Target Detection

Target detection is one of the most important applications of hyperspectral imagery (HSI). The traditional target detection techniques usually discard the spatial information of the target, resulting in a lower accuracy of detection. A novel simultaneous sparse representation model is proposed for HSI target detection. The proposed approach applies the theory and algorithm of mixed-norm to the hyperspectral target detection. By considering the combination of spectral information and spatial context of HSI, a model with a mixed-norm regularizaton based on the simultaneous sparse representation is proposed. And this model is finally solved via alternating direction mehtod of multipliers (ADMM) efficiently. The effectiveness and accuracy of the proposed simultaneous sparse representation model and algorithm are demonstrated by experimental results on a real hyperspectral images.