Sharpening Thermal Imageries: A Generalized Theoretical Framework From an Assimilation Perspective
Sharpening Thermal Imageries: A Generalized Theoretical Framework From an Assimilation Perspective
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DOI：
10.1109/tgrs.2010.2060342
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发表时间：
2011-02
影响因子：
8.2
通讯作者：
W. Zhan;Yunhao Chen;Ji Zhou;Jing Li;Wenyu Liu-
中科院分区：
文献类型：
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作者：
W. Zhan;Yunhao Chen;Ji Zhou;Jing Li;Wenyu Liu-
陆面温度在许多领域中起着重要的作用。然而，目前星载传感器的热波段空间分辨率有限，严重阻碍了其潜在的应用。近年来，已经开发了许多旨在将热图像缩小到更精细的空间分辨率水平的方法。本文试图从同化的角度，通过半经验回归和调制整合技术，为它们构建一个广义的理论框架。基于三个层次的锐化水平，其中包括数字，辐射，和表面温度，其中许多可以纳入这样一个统一的框架作为导数。两个典型的土地覆盖模式被选为案例研究领域，以评估各种内核的能力，代表LST分布。结果表明，这些核之间有很大的差异。单波段核函数依赖于不同的土地覆盖类型，而波段导数核函数在描绘LST变化时在大多数情况下表现得更好。此外，模拟图像的缩放原始热波段的聚合技术进行了重采样，用于验证温度植被干燥指数（TdR）的本地化方法。结果表明，在描述由土壤异常引起的地表温度变化时，该方法具有较好的效果。与中分辨率成像光谱仪和先进星载热辐射反射辐射计数据进行了比较，并与人工神经网络和Gram-Schmidt技术进行了比较。因此，该通用框架在坚实的理论基础上为高效锐化热图像开辟了前景。
Land surface temperature (LST) plays an important role in many fields. However, thermal bands in prevailing sensors that are onboard satellites have limited spatial resolutions, which seriously impede their potential applications. Many approaches that aim to downscale thermal imageries to finer spatial resolution levels have been developed in recent years. This paper managed to construct a Generalized Theoretical Framework from an Assimilation Perspective for them with semiempirical regression and modulation integration techniques. Based on three hierarchical sharpening levels, which include digital number, radiance, and surface temperature, many of them can be brought into such a unified framework as derivatives. Two typical land cover patterns were chosen as case study areas to evaluate the capabilities of various kernels to represent the LST distribution. The results demonstrate that there are great discrepancies among those kernels. The single-band kernels are dependent on different land cover types, while the band-derivative kernels perform better in most circumstances when portraying the LST variations. In addition, the simulated imageries that were resampled by scaling up the original thermal bands with an aggregation technique were utilized to validate a localization approach of temperature vegetation dryness index (TVDI). The results indicate that the TVDI has satisfactory effects when depicting slight LST variations due to soil anomalies. More intercomparisons between the approach presented here and other different methods, including artificial neural network and Gram-Schmidt techniques, were made thoroughly, coupling with the Moderate Resolution Imaging Spectroradiometer and Advanced Spaceborne Thermal Emission Reflection Radiometer data. Consequently, the generalized framework opens up the foreground for sharpening thermal images with high efficiency over a solid theoretical foundation.