Multi-temporal MODIS and Landsat reflectance fusion method based on super-resolution reconstruction

doi:10.11834/jrs.20132016

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Multi-temporal MODIS and Landsat reflectance fusion method based on super-resolution reconstruction
Vol. 17, Issue 3, Pages: 590-608(2013)
Published：2013
DOI： 10.11834/jrs.20132016
稿件说明：

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[1]赵永光,黄波,汪超亮.基于超分辨率重建的多时相MODIS与Landsat反射率融合方法[J].遥感学报,2013,17(03):590-608. DOI： 10.11834/jrs.20132016.

Multi-temporal MODIS and Landsat reflectance fusion method based on super-resolution reconstruction[J]. Journal of Remote Sensing, 2013, 17(3): 590-608. DOI： 10.11834/jrs.20132016.

摘要

提出一种基于超分辨率重建的MODIS与Landsat反射率图像融合方法

以STARFM算法与超分辨率重建为基础

使用观测的MODIS和Landsat地表反射率图像预测给定时刻的Landsat合成反射率图像。该方法利用基于稀疏表示的超分辨率重建方法对MODIS图像进行分辨率增强

实验结果表明这一操作能够增加原MODIS图像的空间细节

有助于提高STARFM算法的预测精度;另一方面

考虑输入两个基时刻图像相差较大时原STARFM算法预测的反射率会存在"时间平滑"的问题

限制每次只使用一个基时刻MODIS和Landsat图像对进行STARFM预测

使用逐图像块选择策略

从由两个基时刻图像分别进行预测得到的两组预测图像中选择最优的预测

同样得到了优于STARFM算法的预测结果。

Abstract

A new Moderate Resolution Imaging Spectroradiometer （ MODIS） and Landsat reflectance fusion method is proposed based on the Spatial and Temporal Adaptive Reflectance Fusion Model （ STARFM） and super-resolution reconstruction

which fuse observed MODIS and Landsat images to produce a Landsat synthetic reflectance image at the prediction date. Super-resolution r econstruction via sparse representation is first applied to enhance the resolution of a MODIS image. The results show that this o peration can enhance the spatial details of the original MODIS image and can improve the prediction accuracy of the STARFM algorithm. On the other hand

considering the problem of"temporal smoothing"attributed to large differences between two input pairs of MODIS and Landsat images

this method adds a patch-based selection strategy to the original STARFM algorithm. This strategy constrains each prediction of STARFM to use only one pair of MODIS and Landsat images at a base date. The optimal prediction of each patch is then selected from two images

which are predicted by two input pairs of MODIS and Landsat images. The results show that the proposed method outperforms the original STARFM algorithm in terms of prediction accuracy.