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Retrieval of the Pixel Component Temperatures from Multi-Band Thermal Infrared Image Using Bayesian Inversion Technique

机译:使用贝叶斯反转技术从多频带热红外图像中检索像素分量温度

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Majority of pixels, in the nature, are non-isothermal in three dimensions, especially for the pixels in meter-scale, tens-meter-scale or hundreds-meter-scale which are paid extensive attention by the researchers in geoscience field. The three-dimensional non-isothermal phenomenon even exists in some pixels in centimeter-scale. For the geosciencific researches, it is significant to determine the component temperatures of a pixel precisely. The airborne WSIS (Wide Spectrum Imaging Spectrometer) data with VNIR (visible-near infrared), SWIR (short-wave infrared) and TIR (thermal infrared) bands were used in the study. First, the components of all the pixels in the image were determined by the linear mixing method. Second, each component emissivity of each pixel was calculated based on an emissivity priori knowledge base. Last, a temperature and emissivity separation algorithm was used to inverse the mean temperature of each pixel, regarded as initial value, the Planck function was linearized to construct a multi-band equation set, and the component temperatures of every pixel were inversed by the Bayesian retrieval technique. The results suggest that the inversion precision of the pixel component temperatures is improved effectively by the Bayesian retrieval technique with the assistance of the VNIR and SWIR hyperspectral remote sensing data.
机译:在本质上,大多数像素是三维的非等温,特别是对于米级,Tens-mets-scale或数百米的像素,这是由地球科学领域的研究人员获得广泛的关注。三维非等温现象甚至存在于厘米级的一些像素中。对于地质学研究,很大的是,精确地确定像素的组分温度。使用VNIR(可见近红外线),SWIR(短波红外)和TIR(热红外)带中使用的空中WSIS(宽谱成像光谱仪)数据。首先,通过线性混合方法确定图像中的所有像素的组件。其次,基于发射率先验知识库计算每个像素的每个分量发射率。最后,使用温度和发射率分离算法对被视为初始值的每个像素的平均温度来逆,普朗克函数被线性化以构建多频段方程组,并且每个像素的组件温度由贝叶斯逆变检索技术。结果表明,贝叶斯检索技术在VNIR和SWIR高光谱遥感数据的帮助下有效地提高了像素分量温度的反转精度。

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