Inversion of Remote Sensing Data Using Multiple Ratios of Spectral Radiation Intensities and Neural Networks
S. Cińôszczyk
Institute of Electronics and Information Technology, Lublin University of Technology, Nadbystrzycka 38A, 20-618 Lublin, Poland
Received: May 16, 2016; In final form: May 23, 2017
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The article presents a method for determining the content and temperature on the basis of spectra from remote measurements. The technique uses measurements of a high resolution radiation spectrum that allows the visibility of the individual rotational lines of gases such as CO2, used here in the range of 2470-2495 cm-1. At the same time a new algorithm is applied of pre-processing the spectrum, involving the use of multiple ratios of intensity at several wavenumbers as input to an inverse model based on neural networks. Due to it, the dimensionality of the input can significantly be reduced. Additionally, the data interpreted do not have to be measured in units of spectral radiance. Thus only the calibration of the sensitivity of the spectrometer at various wavelengths is required. The neural models were constructed on the basis of data from the simulation. The proposed method works with a uniform layer of radiating gas for determining the temperature and CO2 content. For a non-uniform layer it is possible to determine the line-of-sight temperature profile and average gas content. The method can be extended to different spectral ranges and to other gases present in substantial quantities in the exhaust gases of various processes.

DOI: 10.12693/APhysPolA.131.1454
PACS/topics: remote sensing, infrared spectrometers, infrared spectra, computer modelling and simulation