Assessing the Application of Cloud–Shadow Atmospheric Correction Algorithm on HICO

Amin, Ruhul; Lewis, D. T.; Gould, Richard W.; Hou, Weilin; Lawson, Adam; Ondrusek, Michael; Arnone, Robert

doi:10.1109/tgrs.2013.2264166

Cited by 19 publications

(14 citation statements)

References 23 publications

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“…Hyperspectral imaging is well suited to investigating water quality in coastal environments where a local scale is required in the case of optically complex coastal waters (Amin et al, 2014;Gitelson et al, 2011;Gao et al, 2009). For these applications, the water-leaving reflectance has to be obtained by accurate atmospheric modelling because only 10 % of the total radiance received by sensor come from the water target (Antoine and Morel, 1999).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2015

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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 waterleaving 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 r 2 = 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.

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Section: Introductionmentioning

confidence: 99%

“…Significant errors were also obtained when software dedicated to the atmospheric correction of hyperspectral data (i.e. ATREM) was applied to coastal water (Amin et al, 2014;Gitelson et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2015

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“…Sola et al, [15] deal with a speedy procedure for the geometric correction of hyperspectral MIVIS images using Polynomial Model (PM) and the model of Rational Functions (RFM). The main aim is to get an effective geometric correction of MIVIS images by way of an optimal compromise of result precision and elaboration time and can be applied for large areas.…”

Section: Pre-processingmentioning

confidence: 99%

Remote Sensing Satellite Image Processing Techniques for Image Classification: A Comprehensive Survey

Sowmya¹,

Shenoy²,

Venugopal³

2017

IJCA

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This paper is a brief survey of advance technological aspects of Digital Image Processing which are applied to remote sensing images obtained from various satellite sensors. In remote sensing, the image processing techniques can be categories in to four main processing stages: Image preprocessing, Enhancement, Transformation and Classification. Image pre-processing is the initial processing which deals with correcting radiometric distortions, atmospheric distortion and geometric distortions present in the raw image data. Enhancement techniques are applied to preprocessed data in order to effectively display the image for visual interpretation. It includes techniques to effectively distinguish surface features for visual interpretation. Transformation aims to identify particular feature of earth's surface and classification is a process of grouping the pixels, that produces effective thematic map of particular land use and land cover.

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“…Future hyperspectral sensors include Precursore Iperspettrale della Missione Operativa (PRISMA) [129], the Hyperspectral Imager Suite (HISUI) [130], the Environmental Mapping and Analysis Program (EnMap) [89], the Hyperspectral Infrared Imager (HyspIRI) [21] and the Ocean Radiometer for Carbon Assessment (ORCA), which is a prototype being designed to meet the requirements of NASA's Aerosol-Cloud-Ecosystem (ACE) and PACE missions [131]. These sensors possess high spatial and spectral resolutions, including SWIR bands with a SNR high enough to validate the black pixel assumption (see Section 6.1 for a definition) over turbid waters [132]. One drawback is the temporal resolution they provide, which is always more than a few days and, therefore, inadequate for certain applications, such as monitoring algal blooms [114].…”

Section: Sensor Resolutionsmentioning

confidence: 99%

Sensor Capability and Atmospheric Correction in Ocean Colour Remote Sensing

et al. 2015

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Accurate correction of the corrupting effects of the atmosphere and the water's surface are essential in order to obtain the optical, biological and biogeochemical properties of the water from satellite-based multi-and hyper-spectral sensors. The major challenges now for atmospheric correction are the conditions of turbid coastal and inland waters and areas in which there are strongly-absorbing aerosols. Here, we outline how these issues can be addressed, with a focus on the potential of new sensor technologies and the opportunities for the development of novel algorithms and aerosol models. We review hardware developments, which will provide qualitative and quantitative increases in spectral, spatial, radiometric and temporal data of the Earth, as well as measurements from other sources, such as the Aerosol Robotic Network for Ocean Color (AERONET-OC) stations, bio-optical sensors on Argo (Bio-Argo) floats and polarimeters. We provide an overview of the state of the art in atmospheric correction algorithms, highlight recent advances and discuss the possible potential for hyperspectral data to address the current challenges.

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Assessing the Application of Cloud–Shadow Atmospheric Correction Algorithm on HICO

Cited by 19 publications

References 23 publications

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

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

Remote Sensing Satellite Image Processing Techniques for Image Classification: A Comprehensive Survey

Sensor Capability and Atmospheric Correction in Ocean Colour Remote Sensing

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