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Improved VHR Urban Area Mapping Exploiting Object Boundaries

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4 Author(s)

In this paper, a mapping procedure exploiting object boundaries in very high-resolution (VHR) images is proposed. After discrimination between boundary and nonboundary pixel sets, each of the two sets is separately classified. The former are labeled using a neural network (NN), and the shape of the pixel set is finely tuned by enforcing a few geometrical constraints, while the latter are classified using an adaptive Markov random field (MRF) model. The two mapping outputs are finally combined through a decision fusion process. Experimental results on hyperspectral and satellite VHR imagery show the superior performance of this method over conventional NN and MRF classifiers.

Published in:

Geoscience and Remote Sensing, IEEE Transactions on  (Volume:45 ,  Issue: 8 )

Date of Publication:

Aug. 2007

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