Spatio-Temporal Resolution Enhancement for Geostationary Microwave Data
Spatio-Temporal Resolution Enhancement for Geostationary Microwave Data
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DOI：
10.1109/microrad49612.2020.9342539
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
I. Yanovsky;Jing Qin;B. Lambrigtsen
中科院分区：
文献类型：
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作者：
I. Yanovsky;Jing Qin;B. Lambrigtsen
在本文中，我们提供了一个公式，以提高遥感图像序列的时空分辨率。这样的图像序列可以被传感器捕获，该传感器将物理场景与时空点扩展函数进行卷积，其二维空间分量是微波仪器的点扩展函数，其一维时间分量是传感器曝光时间为支撑的矩形核。我们在时空域中执行分辨率增强，而不是为每个观测解决反卷积问题。同时时空优化实现了更高效、更精确的重构。提出的反卷积方法采用全变分正则化，并通过Split-Bregman优化算法求解公式。在我们的实验中，我们使用了一个模拟的飓风微波图像序列，并证明了所提出的方法与观测序列相比提高了精度。
In this paper, we provide a formulation for enhancing the spatio-temporal resolution of a remote sensing sequence of images. Such an image sequence could be captured by a sensor that convolves a physical scene with a spatio-temporal point spread function whose two-dimensional spatial component is the microwave instrument’s point spread function and whose one-dimensional temporal component is the rectangular kernel with sensor exposure time as its support. We perform resolution enhancement in the space-time domain, as opposed to solving the deconvolution problem for each observation. Simultaneous space-time optimization achieves a more efficient and more accurate reconstruction. The proposed deconvolution method employs total variation regularization and solves the formulation via the Split-Bregman optimization algorithm. In our experiments, we use a simulated microwave image sequence of a hurricane and demonstrate that the proposed methodology improves the accuracy when compared to the observed sequence.