一种基于分层策略的时空融合模型

A hierarchical spatial-temporal fusion model

时空数据融合能够有效提高高空间分辨率遥感数据的时间分辨率,但是目前广泛使用的时空自适应反射率融合模型在突变区域的预测效果不佳。针对这一问题,提出一种基于分层策略的时空融合模型(hierarchical spatial-temporal fusion model,H-STFM)。该模型首先根据相邻时刻低空间分辨率数据的反射率差值,将待预测的目标像元分为物候变化像元和突变像元;然后对物候变化像元进行线性回归预测,对突变像元进行加权滤波预测;最后将物候变化和突变区域的预测结果利用优化的时间加权函数融合生成最后预测图像。以两组中分辨率遥感数据MODIS和Landsat图像为基础数据进行实验对H-STFM模型进行了定性与定量评价。结果表明,提出模型的实验结果在方差误差与相对无量纲全局误差方面表现明显优于时空自适应融合模型。

The temporal resolution of high spatial resolution remote sensing data can be effectively improved by spatio-temporal fusion of remote sensing data.However,the most widely used spatial and temporal adaptive reflectance fusion model(STARFM)fails to achieve highly accurate prediction effects for areas with abrupt changes at present.Given this,this paper proposed a hierarchical spatial-temporal fusion model(H-STFM).In this model,the target pixels to be predicted are divided into pixels with phenological change and pixels with abrupt changes,which are predicted using linear regression and weighted filtering methods,respectively.Then the prediction results of the two types of pixels are fused using an optimized time weighted function to form the final prediction map.The H-STFM proposed in this paper was qualitatively and quantitatively assessed using two sets of medium-resolution remote sensing images from moderate resolution imaging spectrometer(MODIS)and Landsat satellite.As indicated by the experiment results,H-STFM is significantly superior to STARFM in terms of structural similarity and relative dimensionless global error.