目的遥感图像融合是将一幅高空间分辨率的全色图像和对应场景的低空间分辨率的多光谱图像,融合成一幅在光谱和空间两方面都具有高分辨率的多光谱图像。为了使融合结果在保持较高空间分辨率的同时减轻光谱失真现象,提出了自适应的权重注...目的遥感图像融合是将一幅高空间分辨率的全色图像和对应场景的低空间分辨率的多光谱图像,融合成一幅在光谱和空间两方面都具有高分辨率的多光谱图像。为了使融合结果在保持较高空间分辨率的同时减轻光谱失真现象,提出了自适应的权重注入机制,并针对上采样图像降质使先验信息变得不精确的问题,提出了通道梯度约束和光谱关系校正约束。方法使用变分法处理遥感图像融合问题。考虑传感器的物理特性,使用自适应的权重注入机制向多光谱图像各波段注入不同的空间信息,以处理多光谱图像波段间的差异,避免向多光谱图像中注入过多的空间信息导致光谱失真。考虑到上采样的图像是降质的,采用局部光谱一致性约束和通道梯度约束作为先验信息的约束,基于图像退化模型,使用光谱关系校正约束更精确地保持融合结果的波段间关系。结果在Geoeye和Pleiades卫星数据上同6种表现优异的算法进行对比实验,本文提出的模型在2个卫星数据上除了相关系数CC(correlation coefficient)和光谱角映射SAM(spectral angle mapper)评价指标表现不够稳定,偶尔为次优值外,在相对全局误差ERGAS(erreur relative globale adimensionnelle de synthèse)、峰值信噪比PSNR(peak signal-tonoise ratio)、相对平均光谱误差RASE(relative average spectral error)、均方根误差RMSE(root mean squared error)、光谱信息散度SID(spectral information divergence)等评价指标上均为最优值。结论本文模型与对比算法相比,在空间分辨率提升和光谱保持方面都取得了良好效果。展开更多
This paper studies the tracking performance of the single-input single-output （SISO）, finite dimensional, linear and time-invariant （LTI） system over an additive white Gaussian noise （AWGN） channel with finite c...This paper studies the tracking performance of the single-input single-output （SISO）, finite dimensional, linear and time-invariant （LTI） system over an additive white Gaussian noise （AWGN） channel with finite control energy and channel input energy constraint. A new performance index is proposed which is minimized over all stabilizing two-degree-of-freedom controllers. The explicit expressions of the lower bound of the tracking performance and the minimum of signal-to-noise required are obtained. The results show that the lower bound is correlated to the unstable pole, nonminimum phase zero and the channel scaling factor. Finally, one example is given to validate the conclusions by adopting the special inner-outer factorization.展开更多
文摘目的遥感图像融合是将一幅高空间分辨率的全色图像和对应场景的低空间分辨率的多光谱图像,融合成一幅在光谱和空间两方面都具有高分辨率的多光谱图像。为了使融合结果在保持较高空间分辨率的同时减轻光谱失真现象,提出了自适应的权重注入机制,并针对上采样图像降质使先验信息变得不精确的问题,提出了通道梯度约束和光谱关系校正约束。方法使用变分法处理遥感图像融合问题。考虑传感器的物理特性,使用自适应的权重注入机制向多光谱图像各波段注入不同的空间信息,以处理多光谱图像波段间的差异,避免向多光谱图像中注入过多的空间信息导致光谱失真。考虑到上采样的图像是降质的,采用局部光谱一致性约束和通道梯度约束作为先验信息的约束,基于图像退化模型,使用光谱关系校正约束更精确地保持融合结果的波段间关系。结果在Geoeye和Pleiades卫星数据上同6种表现优异的算法进行对比实验,本文提出的模型在2个卫星数据上除了相关系数CC(correlation coefficient)和光谱角映射SAM(spectral angle mapper)评价指标表现不够稳定,偶尔为次优值外,在相对全局误差ERGAS(erreur relative globale adimensionnelle de synthèse)、峰值信噪比PSNR(peak signal-tonoise ratio)、相对平均光谱误差RASE(relative average spectral error)、均方根误差RMSE(root mean squared error)、光谱信息散度SID(spectral information divergence)等评价指标上均为最优值。结论本文模型与对比算法相比,在空间分辨率提升和光谱保持方面都取得了良好效果。
基金partially supported by the National Natural Science Foundation of China (Nos. 61073025, 61073065, 61100076, 61170024, 61272114)
文摘This paper studies the tracking performance of the single-input single-output （SISO）, finite dimensional, linear and time-invariant （LTI） system over an additive white Gaussian noise （AWGN） channel with finite control energy and channel input energy constraint. A new performance index is proposed which is minimized over all stabilizing two-degree-of-freedom controllers. The explicit expressions of the lower bound of the tracking performance and the minimum of signal-to-noise required are obtained. The results show that the lower bound is correlated to the unstable pole, nonminimum phase zero and the channel scaling factor. Finally, one example is given to validate the conclusions by adopting the special inner-outer factorization.
