Combining hopfield neural network and contouring methods to enhance super-resolution mapping
Journal
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Journal Volume
5
Journal Issue
5
Pages
1403-1417
Date Issued
2012
Author(s)
Abstract
The mixed pixel problem may be reduced through the use of a soft image classification and super-resolution mapping analyses. Here, the positive attributes of two popular super-resolution mapping methods, based on contouring and the Hopfield neural network, are combined. For a binary classification scenario, the method is based on fitting a contour of equal class membership to a pre-final output of a standard Hopfield neural network. Analyses of simulated and real image data sets show that the proposed method is more accurate than the standard contouring and Hopfield neural network based methods, with error typically reduced by a factor of two or more. The sensitivity of the Hopfield neural network based approaches to the setting of a gain function is also explored.
Type
journal article