Retrieval of Wind Wave Elevation Spectra From Sunglint Data

Cureton, Geoff; Anderson, Stuart; Lynch, M. J.; McGann, B.T.

doi:10.1109/tgrs.2007.900676

Cited by 15 publications

(4 citation statements)

References 11 publications

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“…This behavior agrees with the results presented by Álvarez-Borrego (1993) and Geoff Cureton et al 2007, andMartin-Atienza (2010) for the Gaussian case.…”

Section: Discussionsupporting

confidence: 92%

Statistical Properties of Surface Slopes via Remote Sensing

Álvarez-Borrego¹,

Martin-Atienza²

2012

Remote Sensing - Advanced Techniques and Platforms

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“…This behavior agrees with the results presented by Álvarez-Borrego (1993) and Geoff Cureton et al 2007, andMartin-Atienza (2010) for the Gaussian case.…”

Section: Discussionsupporting

confidence: 92%

Statistical Properties of Surface Slopes via Remote Sensing

Álvarez-Borrego¹,

Martin-Atienza²

2012

Remote Sensing - Advanced Techniques and Platforms

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“…The reflected component of the radiance has never penetrated the water surface, so spectrally it carries no information about sub-surface features and it simplifies analysis to remove it. Although note that other remote sensing methods can usefully infer wave energy and bathymetry from the effect on wave glint patterns (Cureton et al 2007;Splinter and Holman 2009).…”

Section: Sunglint Correctionmentioning

confidence: 99%

Hyperspectral Applications

Hedley¹

2013

Coral Reef Remote Sensing

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Hyperspectral approaches are at the technological forefront of optical remote sensing of coral reef environments. Currently most hyperspectral data acquisition employs instruments mounted on airplanes, but in the coming years several planned satellite instruments will increase data availability for hyperspectral analysis of reefs. At the simplest level, hyperspectral data permits classification techniques to derive greater number of classes at higher accuracy than multispectral data can support. Alternatively, full spectral reflectance profiles at each pixel allow band-ratio or derivative approaches to look for features of benthic types that occur at specific wavelengths. But while the feasibility of this approach is supported by in situ data, there have been relatively few successfully demonstrated image analyses. Beyond this, working with full spectral reflectance profiles has stimulated exciting new model-based methods that aim to tease apart depth, benthic type and water quality parameters simultaneously. These methods can also incorporate uncertainty propagation, so that error bars can be placed on each derived parameter at every image pixel. Working with hyperspectral data takes coral reef remote sensing to the edge of what can be achieved by per-pixel optical analysis. Natural variations in the reflectance of benthic types and water column properties become limiting and fundamentally confound some objectives. This prompts future developments to look at analyzing spatial patterns and also to establish the cost-benefit ratio of the integration of other data, such as sonar and LiDAR data.

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“…The various papers cited present derivations in one and two dimensions and under other various conditions, and conditions under which inversion is possible are discussed. A series of papers by Cureton [46][47][48][49] corrects the model from [39], applies the model to real data, and extends the theory to a higher order. Additionally, Weber [50] gives theoretical discussion of an imaging system for viewing objects underwater through rough seas (most citations are in Russian).…”

Section: Legacy Of Cox and Munkmentioning

confidence: 99%

Characterization of Sun Glitter Statistics in Ocean Video

Rainey¹,

Hallenborg²

2013

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OBJECTIVESunlight reflected on the moving sea surface, often referred to as glitter, is usually considered a hindrance to analysis of ocean scenes, and glitter removal algorithms are often employed in pre-processing of ocean imagery. However, glitter statistics can provide valuable insight into sea surface behavior, both from the standpoint of characterizing image "clutter" and gaining a better understanding of sea surface roughness. In this work, sun glitter is observed through shore-based video of the ocean collected at various optical wavelengths (visible and infrared), and various statistical properties of the collected video are analyzed. RESULTS A MATLAB® tool has been developed with which to analyze several key statistics of the collected video. This report details the functionality of this tool and demonstrates the behavior of a number of spatial-, temporal-, and spectral-domain statistics of glitter video. A detailed background on previous glitter-related research is also given.

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Retrieval of Wind Wave Elevation Spectra From Sunglint Data

Cited by 15 publications

References 11 publications

Statistical Properties of Surface Slopes via Remote Sensing

Statistical Properties of Surface Slopes via Remote Sensing

Hyperspectral Applications

Characterization of Sun Glitter Statistics in Ocean Video

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