The use of spatial constraints in the derivation of mesoscale sea surface current fields from multi-sensor satellite data

Seppke, Benjamin; Gade, Martin; Dreschler-Fischer, Leonie S.

doi:10.1109/igarss.2010.5650882

Cited by 1 publication

(2 citation statements)

References 9 publications

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“…Due to the matching strategy, the first case occurs more often than the second one and cannot be compensated by low-level post-processing steps [7]. The origin of these erroneous motion measurements is the tracking of unsuitable features.…”

Section: Derivation Of High-resolution Sea Surface Currents From Satementioning

confidence: 99%

“…For this decision we need to take different sources of knowledge into account. In [7] we have shown that low level image processing algorithms can be used to improve the derived motion fields by means of smoothness. However there are still cases where this correction may result in unwanted motion measurements that do not correspond to the sea surface currents.…”

Section: Introductionmentioning

confidence: 99%

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A knowledge based framework for the detection of measurement uncertainties in derived sea surface current fields

Seppke

Dreschler-Fischer

Wessel

et al. 2010

2010 IEEE International Geoscience and Remote Sensing Symposium

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In [6] we presented an approach to measure sea surface currents from satellite images by computing the displacement vectors of distinctive image features or measuring the optical flow between successive images of the same scene. These algorithms are based on the assumption that the image flow observed is caused by the motion of surface films with the currents to be measured. Optical flow due to other image features, e.g. ship wakes, does not correspond to the motion field and causes systematic errors. The decision whether a derived measurement is valid or not by domain experts is very timeconsuming. We present first steps towards an automatic knowledge-based approach that uses description logic to validate the measurements of sea surface currents. The terminological knowledge is based on the expert's knowledge of the domain in conjunction with a geographical database containing factual knowledge about the scene. The combination of different sources of knowledge makes it possible to infer about the validity of sea surface current measurements from image data by reasoning based on the local context of the image features.

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Section: Derivation Of High-resolution Sea Surface Currents From Satementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A knowledge based framework for the detection of measurement uncertainties in derived sea surface current fields

Seppke

Dreschler-Fischer

Wessel

et al. 2010

2010 IEEE International Geoscience and Remote Sensing Symposium

Self Cite

View full text Add to dashboard Cite

show abstract

The use of spatial constraints in the derivation of mesoscale sea surface current fields from multi-sensor satellite data

Cited by 1 publication

References 9 publications

A knowledge based framework for the detection of measurement uncertainties in derived sea surface current fields

A knowledge based framework for the detection of measurement uncertainties in derived sea surface current fields

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