Abstract
Contradictions in spatial resolution and temporal coverage emerge from earth observation remote sensing images due to limitations in technology and cost. Therefore, how to combine remote sensing images with low spatial yet high temporal resolution as well as those with high spatial yet low temporal resolution to construct images with both high spatial resolution and high temporal coverage has become an important problem called spatio-temporal fusion problem in both research and practice. A Multi-Dictionary Bayesian Spatio-Temporal Reflectance Fusion Model (MDBFM) has been proposed in this paper. First, multiple dictionaries from regions of different classes are trained. Second, a Bayesian framework is constructed to solve the dictionary selection problem. A pixel-dictionary likehood function and a dictionary-dictionary prior function are constructed under the Bayesian framework. Third, remote sensing images before and after the middle moment are combined to predict images at the middle moment. Diverse shapes and textures information is learned from different landscapes in multi-dictionary learning to help dictionaries capture the distinctions between regions. The Bayesian framework makes full use of the priori information while the input image is classified. The experiments with one simulated dataset and two satellite datasets validate that the MDBFM is highly effective in both subjective and objective evaluation indexes. The results of MDBFM show more precise details and have a higher similarity with real images when dealing with both type changes and phenology changes.
Highlights
MODerate Resolution Imaging Spectroradiometer (MODIS) with a short repeated observation cycle [1] has a spatial resolution ranging from 250 m to 1000 m [2]
SPOT and surface satellite instruments with a spatial resolution ranging from 10 m to 30 m are exceptional sources of satellite information [4].cloud impact and long revisit rate(16 days to a month) [5] limit the detection of changes caused by human activities or rapid surface changes caused by disturbances
To achieve a better spatio-temporal fusion result, additional prior information is incorporated into our fusion model by constructing a multi-dictionary Bayesian framework, where the pixel-dictionary likelihood function and the dictionary-dictionary prior function help dictionaries capture the distinctions between regions
Summary
MODerate Resolution Imaging Spectroradiometer (MODIS) with a short repeated observation cycle (one to eight days) [1] has a spatial resolution ranging from 250 m to 1000 m [2]. The need to better observe and analyze the changes in surface features motivates the emergence of the spatio-temporal fusion technology This technique combines images with a short repeated observation cycle and images with a rich spatial information to construct images with both high spatial and high temporal resolution [6]. This technique has been successfully used to predict high-resolution images in different applications, such as forest change detection [7], urban heat island [8], vegetation indices [9], surface temperature [10] and evapotranspiration [11]. The type changes are considered more challenging to capture than the phenology changes
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