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Urban land cover classification using remote sensing data is quite challenging due to spectrally and spatially complex urban features. The present study describes the potential use of hyperspectral da...
Land Cover is the basis of geographic national conditions monitoring, extracting land cover information timely and accurately has become one of important tasks in the geographic national conditions ...
Markov random field (MRF) is an effective method for description of local spatial-temporal dependence of image and has been widely used in land cover classification and change detection. However, ex...
Land cover classification for high spatial resolution remote sensing images becomes a challenging work. The high spatial resolution remote sensing images have more spatial information. The low or medi...
The integration of different kinds of remotely sensed data, in particular Synthetic Aperture Radar (SAR) and optical satellite imagery, is considered a promising approach for land cover classification...
This study uses the InterIMAGE system and imagery from the QuickBird II optical sensor for the classification of the land cover of two test-sites in the metropolis of São Paulo, Brazil. InterIMA...
A qualitative accuracy assessment for land cover classification was performed to evaluate (i) the performance when applied to topographically and non-topographically corrected Landsat ETM+ images, and...
This paper describes the algorithm used to produce a global land cover product using data sets from the second Advanced Earth Observing Satellite/Global Imager (ADEOS-II/GLI). Although ADEOS-II was in...

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