Supervised vicarious calibration (SVC) of hyperspectral remote-sensing data

Brook, Anna; Dor, Eyal Ben

doi:10.1016/j.rse.2011.02.013

Cited by 68 publications

(34 citation statements)

References 15 publications

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“…These targets represent spatially and radiometrically homogenous natural or artificial ground surface with behaviour of nearLambertian reflector (e.g., bare soil, clay, concrete, etc.) present in the image (Brook and Ben Dor 2011). Just only bright targets (clay and beach volleyball court) were used for verification and vicarious calibration of AISA Eagle data.…”

Section: The Use Of the Chosen Algorithmmentioning

confidence: 99%

Spectral reflectance is a reliable water-quality estimator for small, highly turbid wetlands

Vinciková

Hanuš

Pechar

2015

Wetlands Ecol Manage

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Spectral reflectance from water surfaces was measured in small (0.01-5 km 2 ), turbid, eutrophic fishponds and mesotrophic quarry lakes in the Třeboň basin (South Bohemia, Czech Republic). A spectral scanner for direct field measurements from water surfaces and a hyperspectral airborne scanner were both used. The quarry lakes and fishponds differed in their spectral signature, which reflected the extent of their eutrophication. Their chlorophyll-a (chl-a) concentrations ranged from 2 to 455 lg/l -1 . Various algorithms were tested to best fit the relationships between reflectance patterns and the water-quality parameters used-concentration of chl-a and the total amount of suspended solids. The reflectance ratios at 714 and 650 nm gave the best estimates for chla concentrations, and simple reflectance at near infrared wavelengths, especially at 806 nm, gave the best predictive values for total suspended solid evaluation (r 2 = 0.89). Field surface reflectance and airborne sensing measurements were well correlated; however, airborne reflectance data showed higher variability (r 2 = 0.93 and 0.86, respectively). The results support the validity of reflectance measurements, both field and airborne, as a rapid tool for evaluating water quality in many turbid and greatly disturbed, small water bodies.

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Section: The Use Of the Chosen Algorithmmentioning

confidence: 99%

Spectral reflectance is a reliable water-quality estimator for small, highly turbid wetlands

Vinciková

Hanuš

Pechar

2015

Wetlands Ecol Manage

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“…Therefore, prior to atmospheric correction, the data had to be preprocessed to minimize these effects. The specific preprocessing focused on correcting the cross track illumination effect [9]. To derive surface reflectance from the radiance data, they were corrected for solar irradiance and atmospheric effects such as two-way transmission, multiple scattering, and path radiance using ATCOR 4 software.…”

Section: Hyperspectral Image Datamentioning

confidence: 99%

Mapping hazardous low-pH material in mining environment: Multispectral and hyperspectral aproaches

Kopačková

Chevrel

Bourguignon

et al. 2012

2012 IEEE International Geoscience and Remote Sensing Symposium

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The pH is one of the major chemical parameter affecting results of remediation programs carried on abandoned mines and dumps and one of the major parameters controlling heavy metal mobilization and speciation. Two approaches that contribute to pH mapping were compared. Firstly, highaltitude spectroradiometry (ASTER -Advanced Spaceborne Thermal Emission and Reflection Radiometer satellite data) together with ground-based spectroradiometry (ASD Filedspec spectroradiometer) were employed in order to identify the locations of the most significant sources of AMD discharge at the Sokolov lignite open-pit mines, Czech Republic. As a result, a map with delineated low-pH zones was created. Secondly, the feasibility of mapping surface pH on the basis of airborne multi-flight-line HyMap hyperspectral (HS) datasets was tested. The geochemical and mineral property was linked with emissivity spectra acquired in the field and laboratory. The spectral property of both AMD indicating minerals and the organic component occurring as part of mixtures proved to be identified using spectroscopy and spectral feature fitting (SFF) technique was employed to map the defined endmembers indicating a certain pH ranges in the HS image datasets.

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“…The atmospheric correction performed by using the Smart Nets Vicarious Calibration (SNVC) method that employed ATCOR4 algorithm with radiometric calibration [14]. We estimated the reflectance retrievals performance by calculating the ASDS index [15] for five ground targets (asphalt road, colored concrete roof, Paver flooring, sand and soil).…”

Section: Spectral/spatial Protocolmentioning

confidence: 99%

A spatial/spectral protocol for quality assurance of decompressed hyperspectral data for practical applications

Brook

Ben‐Dor

2010

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing

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A detailed spatial/spectral protocol to test the influence of compressed-decompressed on the hyperspectral image data is presented. The proposed scheme is evaluated by applying an hybrid algorithm (LCT and SPHIT) on AISA-Dual hyperspectral images. For the purpose of reliable thematic results, the preservation and recovering of reflectance spectral features should be on the main concern. Therefore, this protocol contains three stages of data processing: radiance, radiometric/spectral preprocessed radiance, and atmospheric corrected reflectance (generated from radiometric/spectral preprocessed radiance). As for impact on exploitation, we consider IsoData classification, anomaly-detection, and spectral indices as benchmark applications. Additionally, the proposed scheme is compared with common validation approaches, such as spectral response assessment and anomaly-detection techniques.

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Supervised vicarious calibration (SVC) of hyperspectral remote-sensing data

Cited by 68 publications

References 15 publications

Spectral reflectance is a reliable water-quality estimator for small, highly turbid wetlands

Spectral reflectance is a reliable water-quality estimator for small, highly turbid wetlands

Mapping hazardous low-pH material in mining environment: Multispectral and hyperspectral aproaches

A spatial/spectral protocol for quality assurance of decompressed hyperspectral data for practical applications

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