Parameterization of outgoing radiation for quick atmospheric correction of hyperspectral images

2016 ◽  
Vol 29 (9) ◽  
Keyword(s):  
Atmospheric Correction ◽  
Hyperspectral Images ◽  
Outgoing Radiation

scholarly journals AT2ES: Simultaneous Atmospheric Transmittance-Temperature-Emissivity Separation Using Online Upper Midwave Infrared Hyperspectral Images

2021 ◽  
Vol 13 (7) ◽  
pp. 1249
Author(s):  
Sungho Kim ◽  
Jungsub Shin ◽  
Sunho Kim
Keyword(s):  
Air Temperature ◽  
Atmospheric Correction ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Co2 Absorption ◽  
Infrared Band ◽  
Data Regression ◽  
Midwave Infrared ◽  
Novel Method ◽  
Atmospheric Transmittance

This paper presents a novel method for atmospheric transmittance-temperature-emissivity separation (AT2ES) using online midwave infrared hyperspectral images. Conventionally, temperature and emissivity separation (TES) is a well-known problem in the remote sensing domain. However, previous approaches use the atmospheric correction process before TES using MODTRAN in the long wave infrared band. Simultaneous online atmospheric transmittance-temperature-emissivity separation starts with approximation of the radiative transfer equation in the upper midwave infrared band. The highest atmospheric band is used to estimate surface temperature, assuming high emissive materials. The lowest atmospheric band (CO2 absorption band) is used to estimate air temperature. Through onsite hyperspectral data regression, atmospheric transmittance is obtained from the y-intercept, and emissivity is separated using the observed radiance, the separated object temperature, the air temperature, and atmospheric transmittance. The advantage with the proposed method is from being the first attempt at simultaneous AT2ES and online separation without any prior knowledge and pre-processing. Midwave Fourier transform infrared (FTIR)-based outdoor experimental results validate the feasibility of the proposed AT2ES method.

scholarly journals Atmospheric correction of multispectral satellite imagery

2020 ◽  
pp. 4-11
Author(s):  
Leonid Katkovsky
Keyword(s):  
Land Surface ◽  
Satellite Image ◽  
Atmospheric Correction ◽  
Remote Sensing Data ◽  
Sensor Data ◽  
Physical Parameters ◽  
Landsat 8 ◽  
Optical Parameters ◽  
Outgoing Radiation ◽  
Correction Technique

Atmospheric correction is a necessary step in the processing of remote sensing data acquired in the visible and NIR spectral bands.The paper describes the developed atmospheric correction technique for multispectral satellite data with a small number of relatively broad spectral bands (not hyperspectral). The technique is based on the proposed analytical formulae that expressed the spectrum of outgoing radiation at the top of a cloudless atmosphere with rather high accuracy. The technique uses a model of the atmosphere and its optical and physical parameters that are significant from the point of view of radiation transfer, the atmosphere is considered homogeneous within a satellite image. To solve the system of equations containing the measured radiance of the outgoing radiation in the bands of the satellite sensor, the number of which is less than the number of unknowns of the model, it is proposed to use various additional relations, including regression relations between the optical parameters of the atmosphere. For a particular image pixel selected in a special way, unknown atmospheric parameters are found, which are then used to calculate the reflectance for all other pixels.Testing the proposed technique on OLI sensor data of Landsat 8 satellite showed higher accuracy in comparison with the FLAASH and QUAC methods implemented in the well-known ENVI image processing software. The technique is fast and there is using no additional information about the atmosphere or land surface except images under correction.

scholarly journals Effect of the Aerosol Model Assumption on the Atmospheric Correction over Land: Case Studies with CHRIS/PROBA Hyperspectral Images over Benelux

2015 ◽  
Vol 7 (7) ◽  
pp. 8391-8415 ◽  
Author(s):  
Cecilia Tirelli ◽  
Gabriele Curci ◽  
Ciro Manzo ◽  
Paolo Tuccella ◽  
Cristiana Bassani
Keyword(s):  
Case Studies ◽  
Atmospheric Correction ◽  
Hyperspectral Images ◽  
Model Assumption ◽  
Aerosol Model

Evaluation of atmospheric correction using bi-temporal hyperspectral images

2012 ◽  
Vol 60 (1) ◽  
pp. 253-263 ◽  
Author(s):  
Wesley J. Moses ◽  
William D. Philpot
Keyword(s):  
Atmospheric Correction ◽  
Hyperspectral Images

Validation and robustness of an atmospheric correction algorithm for hyperspectral images

2002 ◽  
Author(s):  
Yannick Boucher ◽  
Laurent Poutier ◽  
Veronique Achard ◽  
Xavier Lenot ◽  
Christophe Miesch
Keyword(s):  
Atmospheric Correction ◽  
Hyperspectral Images ◽  
Correction Algorithm

scholarly journals What Can Multifractal Analysis Tell Us about Hyperspectral Imagery?

2020 ◽  
Vol 12 (24) ◽  
pp. 4077
Author(s):  
Michał Krupiński ◽  
Anna Wawrzaszek ◽  
Wojciech Drzewiecki ◽  
Małgorzata Jenerowicz ◽  
Sebastian Aleksandrowicz
Keyword(s):  
Infrared Imaging ◽  
Atmospheric Correction ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
High Spectral Resolution ◽  
Absorption Bands ◽  
Landscape Type ◽  
Spectral Bands ◽  
Before And After ◽  
Additional Value

Hyperspectral images provide complex information about the Earth’s surface due to their very high spectral resolution (hundreds of spectral bands per pixel). Effective processing of such a large amount of data requires dedicated analysis methods. Therefore, this research applies, for the first time, the degree of multifractality to the global description of all spectral bands of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. Subsets of four hyperspectral images, presenting four landscape types, are analysed. In particular, we verify whether multifractality can be detected in all spectral bands. Furthermore, we analyse variability in multifractality as a function of wavelength, for data before and after atmospheric correction. We try to identify absorption bands and discuss whether multifractal parameters provide additional value or can help in the problem of dimensionality reduction in hyperspectral data or landscape type classification.

Influence of atmospheric correction methods at the calculation of vegetation indices on hyperspectral images

2016 ◽  
Vol 906 (13) ◽  
pp. 84-87
Author(s):  
K.I. Zubkova ◽  
◽  
L.I. Permitina ◽  
L.N. Chaban ◽  
◽  
...  
Keyword(s):  
Vegetation Indices ◽  
Atmospheric Correction ◽  
Hyperspectral Images

Using 3D information for atmospheric correction of airborne hyperspectral images of urban areas

2017 ◽  
Author(s):  
Xavier Ceamanos ◽  
Xavier Briottet ◽  
Guillaume Roussel ◽  
Hugo Gilardy ◽  
Karine Adeline
Keyword(s):  
Urban Areas ◽  
Atmospheric Correction ◽  
Hyperspectral Images ◽  
3D Information
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