Superresolution mapping using a hopfield neural network with fused images

Minh, N., Atkinson, P.M. and Lewis, H.G. (2006) Superresolution mapping using a hopfield neural network with fused images IEEE Transactions on Geoscience and Remote Sensing, 44, (3), pp. 736-749. (doi:10.1109/TGRS.2005.861752).


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Superresolution mapping is a set of techniques to increase the spatial resolution of a land cover map obtained by softclassification methods. In addition to the information from the land cover proportion images, supplementary information at the subpixel level can be used to produce more detailed and accurate land cover maps. The proposed method in this research aims to use fused imagery as an additional source of information for superresolution mapping using the Hopfield neural network (HNN). Forward and inverse models were incorporated in the HNN to support a new reflectance constraint added to the energy function. The value of the function was calculated based on a linear mixture model. In addition, a new model was used to calculate the local endmember spectra for the reflectance constraint. A set of simulated images was used to test the new technique. The results suggest that fine spatial resolution fused imagery can be used as supplementary data for superresolution mapping from a coarser spatial resolution land cover proportion imagery.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1109/TGRS.2005.861752
ISSNs: 0196-2892 (print)
Related URLs:
Keywords: fused images, hopfield neural network (HNN) optimization, soft classification, superresolution mapping

ePrint ID: 54951
Date :
Date Event
March 2006Published
Date Deposited: 01 Aug 2008
Last Modified: 16 Apr 2017 17:45
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