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Land surface temperature and emissivity separation (TES) is a key problem in thermal infrared (TIR) remote sensing. Along with the development of civil applications, increasing numbers of man-made low-emissivity materials can be found around our living environment. In addition, the characteristics and variation in properties of those materials should also be concerned. However, there are still few...
To compare and evaluate the performance of iterative spectrally smooth temperature and emissivity separation method (ISSTES) and linear emissivity constraint temperature and emissivity separation method (LECTES) on land surface temperature (LST) and land surface emissivity (LSE) estimation, the simulation data for hyperspectral infrared spectroradiometer are used. The results reveal that the LST can...
Land surface temperature (LST) and land surface emissivity (LSE) are two critical parameters in the physics of land surface processes at different scales. In this paper, an improved linear spectral emissivity constraint method is proposed to make the retrieved emissivity spectra be continuous at the adjacent segments. To evaluate the proposed method, simulation data covering various land surface and...
This paper proposes an edge constrained Markov random field (MRF) method for accurate classification of hyperspectral imagery. The characteristic of the proposed method is using an alterable spatial weighting coefficient, which is acquired based on the edge information, instead of a traditional fixed coefficient. In this way, the spatial contributions in MRF model are different for pixels inside objects...
Land surface temperature and emissivity separation (TES) is a key problem in thermal infrared remote sensing. Current TES methods were proposed and succeeded to apply for the retrieval of land surface temperature and emissivity for the materials with emissivity close to 1. This work addressed the performance of linear spectral emissivity constraint (LSEC) method proposed by wang et al. (2011) for...
Total precipitable water (TPW) is an important atmospheric parameter in many applications. A method was proposed to estimate TPW from thermal infrared hyperspectral data. First, 21 channel groups were selected to retrieve TPW. Then, two indices, namely, the difference- and the ratio-depth in each channel group, were used as the measurement of the water vapor absorption. By multivariate regression,...
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