Atmospheric correction of hyperspectral data over dark surfaces via simulated annealing

2005 ◽  
Author(s):  
Rodolphe Marion ◽  
Rémi Michel ◽  
Christian Faye
Keyword(s):  
Simulated Annealing ◽  
Atmospheric Correction ◽  
Hyperspectral Data

scholarly journals The impact of the microphysical properties of aerosol on the atmospheric correction of hyperspectral data in coastal waters

2015 ◽  
Vol 8 (3) ◽  
pp. 1593-1604 ◽  
Author(s):  
C. Bassani ◽  
C. Manzo ◽  
F. Braga ◽  
M. Bresciani ◽  
C. Giardino ◽  
...  
Keyword(s):  
Accuracy Assessment ◽  
Atmospheric Correction ◽  
Solar Spectrum ◽  
Hyperspectral Data ◽  
High Spectral Resolution ◽  
Quality Analysis ◽  
Microphysical Properties ◽  
Quantitative Remote Sensing ◽  
The Impact ◽  
Aerosol Types

Abstract. Hyperspectral imaging provides quantitative remote sensing of ocean colour by the high spectral resolution of the water features. The HICO™ (Hyperspectral Imager for the Coastal Ocean) is suitable for coastal studies and monitoring. The accurate retrieval of hyperspectral water-leaving reflectance from HICO™ data is still a challenge. The aim of this work is to retrieve the water-leaving reflectance from HICO™ data with a physically based algorithm, using the local microphysical properties of the aerosol in order to overcome the limitations of the standard aerosol types commonly used in atmospheric correction processing. The water-leaving reflectance was obtained using the HICO@CRI (HICO ATmospherically Corrected Reflectance Imagery) atmospheric correction algorithm by adapting the vector version of the Second Simulation of a Satellite Signal in the Solar Spectrum (6SV) radiative transfer code. The HICO@CRI algorithm was applied on to six HICO™ images acquired in the northern Mediterranean basin, using the microphysical properties measured by the Acqua Alta Oceanographic Tower (AAOT) AERONET site. The HICO@CRI results obtained with AERONET products were validated with in situ measurements showing an accuracy expressed by r2 = 0.98. Additional runs of HICO@CRI on the six images were performed using maritime, continental and urban standard aerosol types to perform the accuracy assessment when standard aerosol types implemented in 6SV are used. The results highlight that the microphysical properties of the aerosol improve the accuracy of the atmospheric correction compared to standard aerosol types. The normalized root mean square (NRMSE) and the similar spectral value (SSV) of the water-leaving reflectance show reduced accuracy in atmospheric correction results when there is an increase in aerosol loading. This is mainly when the standard aerosol type used is characterized with different optical properties compared to the local aerosol. The results suggest that if a water quality analysis is needed the microphysical properties of the aerosol need to be taken into consideration in the atmospheric correction of hyperspectral data over coastal environments, because aerosols influence the accuracy of the retrieved water-leaving reflectance.

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 Mapping the Bathymetry of Melt Ponds on Arctic Sea Ice Using Hyperspectral Imagery

2020 ◽  
Vol 12 (16) ◽  
pp. 2623 ◽  
Author(s):  
Marcel König ◽  
Gerit Birnbaum ◽  
Natascha Oppelt
Keyword(s):  
Sea Ice ◽  
Hyperspectral Imagery ◽  
Atmospheric Correction ◽  
Hyperspectral Data ◽  
Arctic Sea Ice ◽  
Atmospheric Conditions ◽  
Melt Pond ◽  
Melt Ponds ◽  
Arctic Sea ◽  
Highly Correlated

Hyperspectral remote-sensing instruments on unmanned aerial vehicles (UAVs), aircraft and satellites offer new opportunities for sea ice observations. We present the first study using airborne hyperspectral imagery of Arctic sea ice and evaluate two atmospheric correction approaches (ATCOR-4 (Atmospheric and Topographic Correction version 4; v7.0.0) and empirical line calibration). We apply an existing, field data-based model to derive the depth of melt ponds, to airborne hyperspectral AisaEAGLE imagery and validate results with in situ measurements. ATCOR-4 results roughly match the shape of field spectra but overestimate reflectance resulting in high root-mean-square error (RMSE) (between 0.08 and 0.16). Noisy reflectance spectra may be attributed to the low flight altitude of 200 ft and Arctic atmospheric conditions. Empirical line calibration resulted in smooth, accurate spectra (RMSE < 0.05) that enabled the assessment of melt pond bathymetry. Measured and modeled pond bathymetry are highly correlated (r = 0.86) and accurate (RMSE = 4.04 cm), and the model explains a large portion of the variability (R2 = 0.74). We conclude that an accurate assessment of melt pond bathymetry using airborne hyperspectral data is possible subject to accurate atmospheric correction. Furthermore, we see the necessity to improve existing approaches with Arctic-specific atmospheric profiles and aerosol models and/or by using multiple reference targets on the ground.

scholarly journals Atmospheric correction of APEX hyperspectral data

2016 ◽  
Vol 20 (1) ◽  
pp. 16-20 ◽  
Author(s):  
Sindy Sterckx ◽  
Kristin Vreys ◽  
Jan Biesemans ◽  
Marian-Daniel Iordache ◽  
Luc Bertels ◽  
...  
Keyword(s):  
Atmospheric Correction ◽  
Hyperspectral Data ◽  
Atmospheric Effects ◽  
Atmospheric Transmission ◽  
Specific Effects ◽  
Correction Process ◽  
Moderate Resolution ◽  
Data Processing Center ◽  
Processing Steps ◽  
Central Data

Abstract Atmospheric correction plays a crucial role among the processing steps applied to remotely sensed hyperspectral data. Atmospheric correction comprises a group of procedures needed to remove atmospheric effects from observed spectra, i.e. the transformation from at-sensor radiances to at-surface radiances or reflectances. In this paper we present the different steps in the atmospheric correction process for APEX hyperspectral data as applied by the Central Data Processing Center (CDPC) at the Flemish Institute for Technological Research (VITO, Mol, Belgium). The MODerate resolution atmospheric TRANsmission program (MODTRAN) is used to determine the source of radiation and for applying the actual atmospheric correction. As part of the overall correction process, supporting algorithms are provided in order to derive MODTRAN configuration parameters and to account for specific effects, e.g. correction for adjacency effects, haze and shadow correction, and topographic BRDF correction. The methods and theory underlying these corrections and an example of an application are presented.

Tafkaa atmospheric correction of hyperspectral data

2004 ◽  
Author(s):  
Marcos J. Montes ◽  
Bo-Cai Gao ◽  
Curtiss O. Davis
Keyword(s):  
Atmospheric Correction ◽  
Hyperspectral Data

scholarly journals Spectral Discrimination and Reflectance Properties of Various Vine Varieties from Satellite, UAV and Proximate Sensors

2015 ◽  
Vol XL-7/W3 ◽  
pp. 31-37 ◽  
Author(s):  
C. Karakizi ◽  
M. Oikonomou ◽  
K. Karantzalos
Keyword(s):  
High Resolution ◽  
Satellite Data ◽  
Atmospheric Correction ◽  
Hyperspectral Data ◽  
Sensor Data ◽  
Sauvignon Blanc ◽  
Multispectral Data ◽  
Spectral Bands ◽  
Spectral Discrimination

An assessment of the spectral discrimination between different vine varieties was undertaken using non-destructive remote sensing observations at the véraison period. During concurrent satellite, aerial and field campaigns, in-situ reflectance data were collected from a spectroradiometer, hyperspectral data were acquired from a UAV and multispectral data from a high-resolution satellite imaging sensor. Data were collected during a three years period (i.e, 2012, 2013 and 2014) over five wine-growing regions, covering more than 1000ha, in Greece. Data for more than twenty different vine varieties were processed and analysed. In particular, reflectance hyperspectral data from a spectroradiometer (GER 1500, Spectra Vista Corporation, 350-1050nm, 512 spectral bands) were calculated from the raw radiance values and then were correlated with the corresponding reflectance observations from the UAV and satellite data. Reflectance satellite data (WorldView-2, 400nm-1040nm, 8 spectral bands, DigitalGlobe), after the radiometric and atmospheric correction of the raw datasets, were classified towards the detection and the discrimination of the different vine varieties. The concurrent observations from in-situ hyperspectral, aerial hyperspectral and satellite multispectral data over the same vines were highly correlated. High correlations were, also, established for the same vine varieties (e.g., Syrah, Sauvignon Blanc) cultivated in different regions. The analysis of in-situ reflectance indicated that certain vine varieties, like Merlot, Sauvignon Blanc, Ksinomavro and Agiorgitiko possess specific spectral properties and detectable behaviour. These observations were, in most cases, in accordance with the classification results from the high resolution satellite data. In particular, Merlot and also Sauvignon Blanc were detected and discriminated with high accuracy rates. Surprisingly different clones from the same variety could be separated (e.g., clones of Syrah), while they were confused with other varieties (e.g., with Riesling).

scholarly journals HYPERSPECTRAL HYPERION IMAGERY ANALYSIS AND ITS APPLICATION USING SPECTRAL ANALYSIS

2015 ◽  
Vol XL-3/W2 ◽  
pp. 169-175 ◽  
Author(s):  
W. Pervez ◽  
S. A. Khan ◽  
Valiuddin
Keyword(s):  
Machine Learning ◽  
Vegetation Index ◽  
Hyperspectral Image ◽  
Atmospheric Correction ◽  
Hyperspectral Data ◽  
Supervised Machine Learning ◽  
Spectral Signature ◽  
Processing Application ◽  
Significant Difference

Rapid advancement in remote sensing open new avenues to explore the hyperspectral Hyperion imagery pre-processing techniques, analysis and application for land use mapping. The hyperspectral data consists of 242 bands out of which 196 calibrated/useful bands are available for hyperspectral applications. Atmospheric correction applied to the hyperspectral calibrated bands make the data more useful for its further processing/ application. Principal component (PC) analysis applied to the hyperspectral calibrated bands reduced the dimensionality of the data and it is found that 99% of the data is held in first 10 PCs. Feature extraction is one of the important application by using vegetation delineation and normalized difference vegetation index. The machine learning classifiers uses the technique to identify the pixels having significant difference in the spectral signature which is very useful for classification of an image. Supervised machine learning classifier technique has been used for classification of hyperspectral image which resulted in overall efficiency of 86.6703 and Kappa co-efficient of 0.7998.

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