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The high-dimensional feature vectors of hyper spectral data often impose a high computational cost as well as the risk of "over fitting" when classification is performed. Therefore it is necessary to ...
The use of satellite imagery in the derivation of land cover information has yielded immense dividends to numerous application fields such as environmental monitoring and modeling, map making and rev...
For the purposes of our study, an ASTER satellite image was used, acquired on 5 October, 2001, with 14 spectral bands resampled at the spatial resolution of 15 m. The study area concerns the geograph...
Hyper-spectral remote sensing increases the volume of information available for research and practice, but brings with it the need for efficient statistical methods in sample spaces of many dimensio...
The development of lidar system, especially incorporated with high-resolution camera components, has shown great potential for urban classification. However, how to automatically select the best featu...
Mapping of Landscape Protection Areas with regard to user requirements for detailed land cover and biotope classes has been limited by the spatial and temporal resolution of Earth observation data. ...
The availability of numerous spectral, spatial, and contextual features renders the selection of optimal features a time consuming and subjective process in object-based image analysis (OBIA). While ...

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