随着高光谱遥感影像(HSI)研究热点的不断上升,影像的去噪工作越显重要。利用HSI影像的特殊特征(频谱间强相关性和低秩子空间等)和混合噪声的特性,提出了基于低秩稀疏矩阵分解的迭代算法,用以除去多种类型的混合噪声。这里提出的算法SLRM...随着高光谱遥感影像(HSI)研究热点的不断上升,影像的去噪工作越显重要。利用HSI影像的特殊特征(频谱间强相关性和低秩子空间等)和混合噪声的特性,提出了基于低秩稀疏矩阵分解的迭代算法,用以除去多种类型的混合噪声。这里提出的算法SLRMS(Subspace Low-Rank Matrix and Sparse Matrix Factorization)充分利用HSI频谱低秩特性,在低秩和稀疏正则化的约束下迭代达到去噪的效果。提出的算法在模拟小数据集Indian pines和大数据集KSC(Kennedy Space Center)上去噪后的视觉效果和定量评价指标,均表现优越且运行所费时间极低。展开更多
In this paper,a space-time correlation based fast regional spectrum sensing(RSS)scheme is proposed to reduce the time and energy consumption of traditional spatial spectrum sensing. The target region is divided into s...In this paper,a space-time correlation based fast regional spectrum sensing(RSS)scheme is proposed to reduce the time and energy consumption of traditional spatial spectrum sensing. The target region is divided into small meshes,and all meshes are clustered into highly related groups using the spatial correlation among them. In each group,some representative meshes are selected as detecting meshes(DMs)using a multi-center mesh(MCM)clustering algorithm,while other meshes(EMs)are estimated according to their correlations with DMs and the Markov modeled dependence on history by MAP principle. Thus,detecting fewer meshes saves the sensing consumption. Since two independent estimation processes may provide contradictory results,minimum entropy principle is adopted to merge the results. Tested with data acquired by radio environment mapping measurement conducted in the downtown Beijing,our scheme is capable to reduce the consumption of traditional sensing method with acceptable sensing performance.展开更多
文摘随着高光谱遥感影像(HSI)研究热点的不断上升,影像的去噪工作越显重要。利用HSI影像的特殊特征(频谱间强相关性和低秩子空间等)和混合噪声的特性,提出了基于低秩稀疏矩阵分解的迭代算法,用以除去多种类型的混合噪声。这里提出的算法SLRMS(Subspace Low-Rank Matrix and Sparse Matrix Factorization)充分利用HSI频谱低秩特性,在低秩和稀疏正则化的约束下迭代达到去噪的效果。提出的算法在模拟小数据集Indian pines和大数据集KSC(Kennedy Space Center)上去噪后的视觉效果和定量评价指标,均表现优越且运行所费时间极低。
基金supported in part by National Natural Science Foundation of China under Grants(61525101,61227801 and 61601055)in part by the National Key Technology R&D Program of China under Grant 2015ZX03002008
文摘In this paper,a space-time correlation based fast regional spectrum sensing(RSS)scheme is proposed to reduce the time and energy consumption of traditional spatial spectrum sensing. The target region is divided into small meshes,and all meshes are clustered into highly related groups using the spatial correlation among them. In each group,some representative meshes are selected as detecting meshes(DMs)using a multi-center mesh(MCM)clustering algorithm,while other meshes(EMs)are estimated according to their correlations with DMs and the Markov modeled dependence on history by MAP principle. Thus,detecting fewer meshes saves the sensing consumption. Since two independent estimation processes may provide contradictory results,minimum entropy principle is adopted to merge the results. Tested with data acquired by radio environment mapping measurement conducted in the downtown Beijing,our scheme is capable to reduce the consumption of traditional sensing method with acceptable sensing performance.
