Fast Bayesian analysis of spatial dynamic factor models for multitemporal remotely sensed imagery
Fast Bayesian analysis of spatial dynamic factor models for multitemporal remotely sensed imagery
复制标题
多时相遥感影像空间动态因子模型的快速贝叶斯分析
DOI：
--
复制
发表时间：
2008
期刊：
影响因子：
--
通讯作者：
Kerrie Mengersen
中科院分区：
文献类型：
--
作者：
Christopher M. Strickland;Daniel P. Simpson;Ian Turner;R. Denham;Kerrie Mengersen
摘要.遥感就是这样一个例子，在这一例子中，随时间和空间而变化的数据集已经变得如此庞大，以致于统计建模者用于应用分析的“标准”方法不再可行。 我们提出了一种贝叶斯方法，它利用最近开发的算法在应用数学，大型时空数据集的分析。特别是，马尔可夫链蒙特卡罗算法提出了空间动态因子模型的有效估计。空间动态因子模型被指定，其中通过使用高斯马尔可夫随机场的因子载荷矩阵的列来模拟空间依赖性。Krylov子空间方法用于利用模型中固有的稀疏矩阵结构。该方法用于分析来自中等成像分光辐射计卫星的遥感数据。特别是，所提出的方法是结合高分辨率图像的分类，在土地类型，在澳大利亚中部昆士兰州的两个地区。
Summary. Remote sensing is one example where data sets that vary across space and time have become so large that ‘standard’ approaches employed by statistical modellers for applied analysis are no longer feasible. We present a Bayesian methodology, which makes use of recently developed algorithms in applied mathematics, for the analysis of large space–time data sets. In particular, a Markov chain Monte Carlo algorithm is proposed for the efficient estimation of spatial dynamic factor models. The spatial dynamic factor model is specified whereby spatial dependence is modelled though the columns of the factor loadings matrix by using a Gaussian Markov random field. Krylov subspace methods are used to take advantage of the sparse matrix structures that are inherent in the model. The methodology is used to analyse remotely sensed data from the Moderate Imaging Spectroradiometer satellite. In particular, the methodology proposed is used in conjunction with high resolution imagery for the classification, in terms of land type, of two regions in central Queensland, Australia.