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Sub-pixel based digital image classification outputs from coarse spatial resolution remote sensing images can be closer to ground information as compared to hard classification outputs. This hasbeen p...
Along with the significant improvement of spatial resolution of remote sensing imagery in the recent years, traditional per-pixel based classification techniques have been facing increasing problems ...
In order to adequately model ecosystems services of the urban environment, it is necessary to accurately inventory urban vegetation abundance and spatial distribution. An object-based, methodological ...
The latest developments in remote sensing analysis software allow new ways of integrating satellite imagery with archive data from aerial photography. This paper shows how this integration is useful i...
High resolution satellite images are associated with large variance and thus, per pixel classifiers often result in poor accuracy especially in delineation of horticultural crops. In this context, obj...
During the last several years, lidar has become a widely used technique for data collection from the earth surface and vegetation canopy being the large volume of high density lidardata the main drawb...
In this paper an object-based classification framework is introduced for the automated monitoring of changes in urban areas. Morphological scale space filtering is embedded in the pro...

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