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Library GeoEye-1 and WorldView-2 pan-sharpened imagery for object-based classification in urban environments

GeoEye-1 and WorldView-2 pan-sharpened imagery for object-based classification in urban environments

GeoEye-1 and WorldView-2 pan-sharpened imagery for object-based classification in urban environments

Resource information

Date of publication
December 2013
Resource Language
ISBN / Resource ID
AGRIS:US201400149779
Pages
2583-2606

The latest breed of very high resolution (VHR) commercial satellites opens new possibilities for cartographic and remote-sensing applications. In fact, one of the most common applications of remote-sensing images is the extraction of land-cover information for digital image base maps by means of classification techniques. The aim of the study was to compare the potential classification accuracy provided by pan-sharpened orthoimages from both GeoEye-1 and WorldView-2 (WV2) VHR satellites over urban environments. The influence on the supervised classification accuracy was evaluated by means of an object-based statistical analysis regarding three main factors: (i) sensor used; (ii) sets of image object (IO) features used for classification considering spectral, geometry, texture, and elevation features; and (iii) size of training samples to feed the classifier (nearest neighbour (NN)). The new spectral bands of WV2 (Coastal, Yellow, Red Edge, and Near Infrared-2) did not improve the benchmark established from GeoEye-1. The best overall accuracy for GeoEye-1 (close to 89%) was attained by using together spectral and elevation features, whereas the highest overall accuracy for WV2 (83%) was achieved by adding textural features to the previous ones. In the case of buildings classification, the normalized digital surface model computed from light detection and ranging data was the most valuable feature, achieving producer's and user's accuracies close to 95% and 91% for GeoEye-1 and VW2, respectively. Last but not least and regarding the size of the training samples, the rule of ‘the larger the better' was true but, based on statistical analysis, the ideal choice would be variable depending on both each satellite and target class. In short, 20 training IOs per class would be enough if the NN classifier was applied on pan-sharpened orthoimages from both GeoEye-1 and WV2.

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Authors and Publishers

Author(s), editor(s), contributor(s)

Aguilar, M.A.
Saldaña, M.M.
Aguilar, F.J.

Publisher(s)
Data Provider