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SAR images of river inundation prove to be very relevant for operational flood management. However, common exploitation of satellite images of floods is generally restricted to a flood extent extracti...
This paper deals with the retrieval of vanishing points in uncalibrated images. Many authors did work on that subject in the computer vision field because the vanishing point represents a major inform...
In the remote sensing data not only have radiation error and geometry error, need geometry correction and radiometric correction, but most of them also have “same object different spectrum”and”same sp...
The maintenance of satellite and spatial data quality is an important task and escalating interest amongst geospatial community. The visualization of perfect earth surface features by relative respons...
For realistic modelling of digital elevation model (DEM) uncertainty, information on the amount and spatial configuration is needed.However, common DEM products are often distributed with global error...
Data quality and uncertainty modeling for spatial data and spatial analyses is regarded as one of the disciplines of geographic information science together with space and time in geography, as well a...
Within the framework of uncertainty-based geographic information science, this paper addresses modeling uncertainties in integrating multiple sources of data, modeling uncertainty in overlay analysis,...
An object-based approach is applied in land-cover feature extraction from high-resolution multi-spectral images and laser scanning data in this research. Objects extracted from high-resolution spect...
Although uncertainties appear in almost every analysis or simulation, they are often not considered or described explicitly. Especially in complex models they can become very large. The neglect of des...
Multispectral remote sensing images are widely used for landuse/landcover (LULC) classification. Performance of such classification practices is normally evaluated through the confusion matrix which...

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