基于时空Markov随机场的遥感影像超分辨率制图研究

项目名称： 基于时空Markov随机场的遥感影像超分辨率制图研究

项目编号： No.41301398

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 天文学、地球科学

项目作者： 李晓冬

作者单位： 中国科学院测量与地球物理研究所

项目金额： 24万元

中文摘要： 遥感影像超分辨率制图是利用遥感数据获得更高空间分辨率地物分类图的过程。当前超分辨率制图主要采用单一时相数据源，模型仅依据亚像元的空间关联特征对求解的亚像元类别标号进行空间约束，结果的不确定性很大。遥感技术具有多尺度观测能力，可以提供研究区不同时间和空间分辨率的遥感影像。针对上述问题，结合较低空间分辨率遥感影像重访周期短以及较高空间分辨率遥感影像地物空间结构信息丰富的优势，提出一种针对较低空间分辨率遥感影像及时更新数据以及较高空间分辨率遥感影像历史数据的超分辨率制图模型，以Markov随机场为理论基础，采用时空邻域系统构建亚像元在时间和空间上的联系，旨在通过增加类别场的时间约束条件减轻模型的不确定性。在此基础上，构建基于时空Markov随机场的超分辨率制图模型，研究模型的基本理论和求解方法，探讨模型参数自适应估计的一般性规律，为进一步推进超分辨率制图的理论研究和实际应用奠定基础。

中文关键词： 超分辨率制图；多尺度分析；遥感；时空融合；变化检测

英文摘要： Super-resolution mapping is a technique to predict land cover maps with a finer spatial resolution than the input remotely sensed images. Present super-resolution mapping models use mono-temporal remotely sensed image as input, and integrate spatial constraints of the sub-pixel labels in the model based on the spatial dependence of sub-pixels. Unfortunately, the uncertainty of the result is still large. Remote sensing technique has its advantages in multi-scale monitoring, and the remotely sensed images with different spatial and temporal resolutions can be provided. According to the aforementioned issues, this project aims to provide a super-resolution mapping model that combines the low-spatial-resolution remotely sensed images which have high-temporal-resolution with fine-spatial-resolution images which can provide useful land cover spatial patterns. Based on Markov random field theory, the proposed model adopts spatial-temporal neighborhood system to spatially and temporally link the sub-pixels, and the model is expected to reduce the uncertainty of the result by using both spatial and temporal constraints of sub-pixels. Based on this issue, the project aims constructing a spatial-temporal Markov random field based super-resolution mapping model, analyzing the theory and solving of the proposed model and pro

英文关键词： super-resolution mapping；multi-scale analysis；remote sensing；spatial-temporal fusion；change detection