A new remote sensing image fusion method based on statistical parameter estimation is proposed in this paper. More specially, Bayesian linear estimation （BLE） is applied to observation models between remote sensing ...A new remote sensing image fusion method based on statistical parameter estimation is proposed in this paper. More specially, Bayesian linear estimation （BLE） is applied to observation models between remote sensing images with different spatial and spectral resolutions. The proposed method only estimates the mean vector and covariance matrix of the high-resolution multispectral （MS） images, instead of assuming the joint distribution between the panchromatic （PAN） image and low-resolution mulUspectral image. Furthermore, the proposed method can enhance the spatial resolution of several principal components of MS images, while the traditional Principal Component Analysis （PCA） method is limited to enhance only the first principal component. Experimental results with real MS images and PAN image of Landsat ETM＋ demonstrate that the proposed method performs better than traditional methods based on statistical parameter estimation, PCA-based method and wavelet-based method.展开更多
基金National Natural Science Foundation of China (Grant Nos. 60672116 and 30370392)the Major State Basic Research Development Program of China (Grant No. 2001CB309400)+1 种基金 HangTian Support Techniques Foundation (Grant No. 2004-1.3-03)Shanghai NSF (Grant No. 04ZR14018)
文摘A new remote sensing image fusion method based on statistical parameter estimation is proposed in this paper. More specially, Bayesian linear estimation （BLE） is applied to observation models between remote sensing images with different spatial and spectral resolutions. The proposed method only estimates the mean vector and covariance matrix of the high-resolution multispectral （MS） images, instead of assuming the joint distribution between the panchromatic （PAN） image and low-resolution mulUspectral image. Furthermore, the proposed method can enhance the spatial resolution of several principal components of MS images, while the traditional Principal Component Analysis （PCA） method is limited to enhance only the first principal component. Experimental results with real MS images and PAN image of Landsat ETM＋ demonstrate that the proposed method performs better than traditional methods based on statistical parameter estimation, PCA-based method and wavelet-based method.
文摘对采用可见光通信(Visible Light Communication,VLC)的车辆定位方法进行了研究,研究、分析并比较了4种基于VLC的车辆定位方法;基于假设的系统模型和接收到的VLC信号数学模型,分析了每种方法所采用的TX位置与系统物理参数的测量过程,利用这些参数与TX位置之间的几何关系构成一个观测模型,获得车辆位置估计;基于VLC定位方法的观测模型得到每种方法关于位置精度的Cramer-Rao下界(Cramer-Rao Lower Bound,CRLB);在一般有限传播延迟、视距(Line of Sight,LoS)和加性高斯白噪声(Additive White Gaussian Noise,AWGN)的VLC信道模型下,对于真实道路的避碰和列队行驶场景,仿真了每种方法的系统物理参数测量,并基于测量结果,对每种方法的定位精度的CRLB进行了评价。
