A sun glint correction method for hyperspectral imagery containing areas with non-negligible water leaving NIR signal

Kutser, Tiit; Vahtmäe, Ele; Praks, Jaan

doi:10.1016/j.rse.2009.06.016

Cited by 102 publications

(86 citation statements)

References 27 publications

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“…The method could not be used at all on the IKONOS data, which has only one NIR band. However, the results presented in [43] suggest that this approach has advantages over other methods in cases where the water-leaving signal is strong, e.g., where vegetation is close to the water surface or the water is less than 2 m deep. shows four wavebands for pixels along the line shown in (a)(i) and (iii) shows a spectral analysis for pixels at each end of the line (i.e., in the glint and no-glint regions).…”

Section: Comments On the Methodsmentioning

confidence: 94%

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Sun Glint Correction of High and Low Spatial Resolution Images of Aquatic Scenes: a Review of Methods for Visible and Near-Infrared Wavelengths

2009

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Sun glint, the specular reflection of light from water surfaces, is a serious confounding factor for remote sensing of water column properties and benthos. This paper reviews current techniques to estimate and remove the glint radiance component from imagery. Methods for processing of ocean color images use statistical sea surface models to predict the glint from the sun and sensor positions and wind data. Methods for higher resolution imaging, used in coastal and shallow water mapping, estimate the glint radiance from the near-infrared signal. The effects of some current methods are demonstrated and possibilities for future techniques are briefly addressed.

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Section: Comments On the Methodsmentioning

confidence: 94%

“…Kutser, Vahtmäe and Praks [43] suggest a variant on these methods, which uses information in the oxygen absorption band at 760 nm to assess the size of the glint. They assume that the amount of glint is proportional to the depth of the oxygen absorption feature, D: …”

Section: The Methods Of Kutser Et Almentioning

confidence: 99%

Sun Glint Correction of High and Low Spatial Resolution Images of Aquatic Scenes: a Review of Methods for Visible and Near-Infrared Wavelengths

2009

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“…Furthermore, classification errors primarily occur at pixels with insufficient sunglint removal. Conventional sunglint removal methods may be suitable to reduce the sunglint and thus also the classification errors [80][81][82]. However, a first attempt to correct the sunglint with the method introduced by Kutser et al, (2009) showed unsatisfactory results.…”

Section: Kelp Detection Results Validated With Diving Transectsmentioning

confidence: 99%

Submerged Kelp Detection with Hyperspectral Data

2016

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Submerged marine forests of macroalgae known as kelp are one of the key structures for coastal ecosystems worldwide. These communities are responding to climate driven habitat changes and are therefore appropriate indicators of ecosystem status and health. Hyperspectral remote sensing provides a tool for a spatial kelp habitat mapping. The difficulty in optical kelp mapping is the retrieval of a significant kelp signal through the water column. Detecting submerged kelp habitats is challenging, in particular in turbid coastal waters. We developed a fully automated simple feature detection processor to detect the presence of kelp in submerged habitats. We compared the performance of this new approach to a common maximum likelihood classification using hyperspectral AisaEAGLE data from the subtidal zones of Helgoland, Germany. The classification results of 13 flight stripes were validated with transect diving mappings. The feature detection showed a higher accuracy till a depth of 6 m (overall accuracy = 80.18%) than the accuracy of a maximum likelihood classification (overall accuracy = 57.66%). The feature detection processor turned out as a time-effective approach to assess and monitor submerged kelp at the limit of water visibility depth.

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“…Modeling of optical water properties for the littoral zone is more complicated due to rapidly changing water depth and/or substrate and higher amounts of Colored Dissolved Organic Matter (CDOM) and/or suspended particles (phytoplankton, seston, and inorganic particles) compared to the deeper portions of oceans. In addition, bottom backscattering in the shallow areas is more significant, which makes the NIR signals more important, especially in areas that contain substantial amount of seagrasses, benthic algae, or phytoplankton (Kutser et al, 2009) and that the conventional Beer-Lambert's exponential light attenuation with depth is not applicable (Holden & LeDrew, 2002).…”

Section: Dilemmas In Remote Sensing Of Shallow Aquatic System and Savmentioning

confidence: 99%

Remote Sensing of Submerged Aquatic Vegetation

Cho¹,

Mishra²,

Wood³

2012

Remote Sensing - Applications

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A sun glint correction method for hyperspectral imagery containing areas with non-negligible water leaving NIR signal

Cited by 102 publications

References 27 publications

Sun Glint Correction of High and Low Spatial Resolution Images of Aquatic Scenes: a Review of Methods for Visible and Near-Infrared Wavelengths

Sun Glint Correction of High and Low Spatial Resolution Images of Aquatic Scenes: a Review of Methods for Visible and Near-Infrared Wavelengths

Submerged Kelp Detection with Hyperspectral Data

Remote Sensing of Submerged Aquatic Vegetation

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