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Received：12 June 2019，
Revised：2019-9-8，
Accepted：15 September 2019，
Published：16 March 2020
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目的
2
时空融合是解决当前传感器无法兼顾遥感图像的空间分辨率和时间分辨率的有效方法。在只有一对精细-粗略图像作为先验的条件下，当前的时空融合算法在预测地物变化时并不能取得令人满意的结果。针对这个问题，本文提出一种基于线性模型的遥感图像时空融合算法。
方法
2
使用线性关系表示图像间的时间模型，并假设时间模型与传感器无关。通过分析图像时间变化的客观规律，对模型进行全局和局部约束。此外引入一种多时相的相似像素搜寻策略，更灵活地选取相似像素，消除了传统算法存在的模块效应。
结果
2
在两个数据集上与STARFM（spatial and temporal adaptive reflectance fusion model）算法和FSDAF（flexible spatiotemporal data fusion）算法进行比较，实验结果表明，在主要发生物候变化的第1个数据集，本文方法的相关系数CC（correlation coefficient）分别提升了0.25%和0.28%，峰值信噪比PSNR（peak signal-to-noise ratio）分别提升了0.153 1 dB和1.379 dB，均方根误差RMSE（root mean squared error）分别降低了0.05%和0.69%，结构相似性SSIM（structural similarity）分别提升了0.79%和2.3%。在发生剧烈地物变化的第2个数据集，本文方法的相关系数分别提升了6.64%和3.26%，峰值信噪比分别提升了2.086 0 dB和2.510 7 dB，均方根误差分别降低了1.45%和2.08%，结构相似性分别提升了11.76%和11.2%。
结论
2
本文方法根据时间变化的特点，对时间模型进行优化，同时采用更加灵活的相似像素搜寻策略，收到了很好的效果，提升了融合结果的准确性。
Objective
2
Fine resolution images with high acquisition frequency play a key role in earth surface observation. However
due to technical and budget limitations
current satellite sensors have a tradeoff between spatial and temporal resolutions. No single sensor can simultaneously achieve a fine spatial resolution and a frequent revisit cycle although a large number of remote sensing instruments with different spatial and temporal characteristics have been launched. For example
Landsat sensors have fine spatial resolutions (15~60 m) but long revisit frequencies (16 days). By contrast
a moderate resolution imaging spectro-radiometer (MODIS) instrument has a frequent revisit cycle (1 day) but a coarse spatial resolution (250~1 000 m). In addition
optical satellite images are frequently contaminated by clouds
cloud shadows
and other atmospheric conditions. These factors limit applications that require data with both high spatial resolution and high temporal resolution. Spatio-temporal satellite image fusion is an effective way to solve this problem. Many spatio-temporal fusion methods have been proposed recently. Existing spatio-temporal data fusion methods are mainly divided into the following three categories:weight function-based methods
unmixing-based methods
and dictionary learning-based methods. All of these methods require at least one pair of observed coarse- and fine-resolution images for training and a coarse-resolution image at prediction date as input data. The output of spatio-temporal fusion methods is a synthetic fine-resolution image at prediction date. All spatio-temporal fusion methods use spatial information from the input fine-resolution images and temporal information from the coarse-resolution images. Unfortunately
existing spatio-temporal fusion methods cannot achieve satisfactory results in accurately predicting land-cover type change with only one pair of fine-coarse prior images. Thus
spatio-temporal satellite image-fusion method based on linear model is proposed to improve the prediction capacity and accuracy
especially for complex changed landscapes.
Method
2
The temporal model is assumed to be independent of sensors
and a linear relationship is used to represent the temporal model between images acquired on different dates. Therefore
the spatio-temporal fusion is transformed into estimating parameters of the temporal model. To accurately capture earth surface change during the period between the input and prediction dates
we carefully analyzed the reasons for the temporal change
and then the temporal change was divided into two types:phonological and land cover type. The former is mainly caused by differences in atmospheric condition
solar angle at different dates
and is global and flat. The latter is mainly caused by the change on the surface
and is local and abrupt. Therefore
parameters of the model from global and local perspectives were estimated. To accurately estimate the parameters
we need to search for similar pixels in the local window to ensure that the pixels used for parameter estimation satisfies spectral consistency. Moreover
considering that the land-cover type may change during the period
we find that the spatial distribution of similar pixels may change at different dates. Therefore
a multi-temporal search strategy is introduced to flexibly select appropriate neighboring pixels. Only pixels that have similar spectral information to the target pixel at both base date and prediction date are considered to be similar pixels
which eliminate the block effect of traditional algorithms. After searching similar pixels and solving the temporal model
the input fine resolution image was combined with the temporal model to predict fine image at target date. The aforementioned strategies make our method achieve good prediction results even if the earth surface changed drastically.
Result
2
We compared our model with two popular spatio-temporal fusion models:spatial and temporal adaptive reflectance fusion model (STARFM) and flexible spatiotemporal data fusion (FSDAF) method on two datasets. The experiment results show that our model outperforms all other methods in both datasets. In the first experiment
the dataset constitutes primarily phenological change. Therefore
all three methods achieve satisfactory results and our method achieves the best result. Quantitative comparisons show that our method achieves high correlation coefficient (CC)
peak signal-to-noise ratio (PSNR)
structural similarity (SSIM)
and lower root mean square error (RMSE). Compared with STARFM and FSDAF
our method increases CC by 0.25% and 0.28%
PSNR by 0.153 1 dB and 1.379 dB
SSIM by 0.79% and 2.3%
and decreases RMSE by 0.05% and 0.69%. In the second experiment
the dataset has undergone dramatic land-cover type change. Therefore
both STARFM and FSDAF have block effects at different levels visually. In quantitative assessment
compared with STARFM and FSDAF
our method increases CC by 6.64% and 3.26%
PSNR by 2.086 dB and 2.510 7 dB
SSIM by 11.76% and 11.2%
and decreases RMSE by 1.45% and 2.08%.
Conclusion
2
In this study
a spatio-temporal satellite image-fusion method based on linear model is proposed. This method uses a linear model to represent the temporal change. By analyzing the characteristics of the temporal change
the temporal model is constrained from local and global perspectives
and the solved model can represent the temporal change accurately. In addition
the method uses a multi-temporal similar pixel search strategy to search for similar pixels more flexibly
thereby eliminating the block effect of previous methods
fully utilizing spectral information in neighboring similar pixels
and improving the accuracy of prediction results. The experimental results show that in terms of visual comparison
compared with two popular spatiotemporal fusion methods
the proposed method can predict land-cover type change more accurately
and our findings are close to the true image. In the quantitative evaluation
our method improves CC
PSNR
RMSE
SSIM
and other indicators to varying degrees in each band.
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