An Unmixing-Based Bayesian Model for Spatio-Temporal Satellite Image Fusion in Heterogeneous Landscapes
An Unmixing-Based Bayesian Model for Spatio-Temporal Satellite Image Fusion in Heterogeneous Landscapes
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DOI：
10.3390/rs11030324
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发表时间：
2019-02
期刊：
影响因子：
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通讯作者：
Jie Xue;Y. Leung;T. Fung
中科院分区：
文献类型：
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作者：
Jie Xue;Y. Leung;T. Fung
对异质景观中的地表动力学的研究通常需要在时间和空间上都具有高分辨率的卫星图像。然而，卫星传感器的设计通常本质上限制了此类图像的可用性。具有高空间分辨率的图像往往具有相对较低的时间分辨率，反之亦然。因此，两种类型图像的融合提供了一种生成空间和时间分辨率高的数据的有用方法。贝叶斯数据融合框架可以基于严格的统计基础生成目标高分辨率图像。然而，现有的贝叶斯数据融合算法，例如STBDF（时空贝叶斯数据融合）-I和-II，并没有完全融合低空间分辨率像素中包含的混合信息，这反过来可能限制其在异构景观中的融合能力。为了增强现有STBDF模型处理异构区域的能力，本研究提出了两种改进的贝叶斯数据融合方法，即ISTBDF-I和ISTBDF-II，它们将基于分解的算法合并到现有的STBDF框架中。使用模拟数据和真实卫星图像，对所提出的算法的性能与STBDF-II进行直观和定量的比较。实验结果表明，所提出的算法生成的时空分辨率图像优于 STBDF-II，特别是在异构区域。它们为进一步增强我们的融合能力指明了方向。
Studies of land surface dynamics in heterogeneous landscapes often require satellite images with a high resolution, both in time and space. However, the design of satellite sensors often inherently limits the availability of such images. Images with high spatial resolution tend to have relatively low temporal resolution, and vice versa. Therefore, fusion of the two types of images provides a useful way to generate data high in both spatial and temporal resolutions. A Bayesian data fusion framework can produce the target high-resolution image based on a rigorous statistical foundation. However, existing Bayesian data fusion algorithms, such as STBDF (spatio-temporal Bayesian data fusion) -I and -II, do not fully incorporate the mixed information contained in low-spatial-resolution pixels, which in turn might limit their fusion ability in heterogeneous landscapes. To enhance the capability of existing STBDF models in handling heterogeneous areas, this study proposes two improved Bayesian data fusion approaches, coined ISTBDF-I and ISTBDF-II, which incorporate an unmixing-based algorithm into the existing STBDF framework. The performance of the proposed algorithms is visually and quantitatively compared with STBDF-II using simulated data and real satellite images. Experimental results show that the proposed algorithms generate improved spatio-temporal-resolution images over STBDF-II, especially in heterogeneous areas. They shed light on the way to further enhance our fusion capability.