Peer-reviewed Article
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Language:
English

Classifying a high resolution image of an urban area using super-object information

In ISPRS Journal of Photogrammetry and Remote Sensing 83
Author: 
Zhixiao
XIE
2013-09

In this study, a multi-scale approach was used for mapping land cover in a high resolution image of an urban area. Pixels and image segments were assigned the spectral, texture, size, and shape information of their super-objects (i.e. the segments that they are located within) from coarser segmentations of the same scene, and this set of super-object information was used as additional input data for image classification. The accuracies of classifications that included super-object variables were compared with the classification accuracies of image segmentations that did not include super-object information. The highest overall accuracy and kappa coefficient achieved without super-object information was 78.11% and 0.727%, respectively. When single pixels or fine-scale image segments were assigned the statistics of their super-objects prior to classification, overall accuracy increased to 84.42% and the kappa coefficient increased to 0.804.

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Publisher: 
Elsevier B.V.
Page Information:
40-49