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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...
This study focuses on the comparison between the classical and object-oriented image classifications of remote sensing imagery in the arid area. Due to its special geographic environment and socio-e...
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 ...
Many applications of remote sensing – like, for example, urban monitoring – require high resolution data for a correct determination of object geometry. These spatial high resolution image data contai...
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 ...
In this study, land cover types in Zonguldak test area were analysed on the basis of the classification results acquired using the pixelbased and object-oriented image analysis approaches. Landsat-7 ...
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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