Super-resolution target identification from remotely sensed images using a Hopfield neural network


Tatem, A.J., Lewis, H.G., Atkinson, P.M. and Nixon, M.S. (2001) Super-resolution target identification from remotely sensed images using a Hopfield neural network. IEEE Transactions on Geoscience and Remote Sensing, 39, (4), 781-96. (doi:10.1109/36.917895).

Download

[img]
Preview
PDF - Publishers print
Download (345Kb)
Original Publication URL: http://dx.doi.org/10.1109/36.917895

Description/Abstract

Fuzzy classification techniques have been developed recently to estimate the class composition of image pixels, but their output provides no indication of how these classes are distributed spatially within the instantaneous field of view represented by the pixel. As such, while the accuracy of land cover target identification has been improved using fuzzy classification, it remains for robust techniques that provide better spatial representation of land cover to be developed. Such techniques could provide more accurate land cover metrics for determining social or environmental policy, for example. The use of a Hopfield neural network to map the spatial distribution of classes more reliably using prior information of pixel composition determined from fuzzy classification was investigated. An approach was adopted that used the output from a fuzzy classification to constrain a Hopfield neural network formulated as an energy minimization tool. The network converges to a minimum of an energy function, defined as a goal and several constraints. Extracting the spatial distribution of target class components within each pixel was, therefore, formulated as a constraint satisfaction problem with an optimal solution determined by the minimum of the energy function. This energy minimum represents a “best guess” map of the spatial distribution of class components in each pixel. The technique was applied to both synthetic and simulated Landsat TM imagery, and the resultant maps provided an accurate and improved representation of the land covers studied, with root mean square errors (RMSEs) for Landsat imagery of the order of 0.09 pixels in the new fine resolution image recorded

Item Type: Article
ISSNs: 0196-2892 (print)
Related URLs:
Keywords: Hopfield neural nets, geophysical signal processing, geophysical techniques, geophysics computing, image classification, remote sensing, terrain mapping
Subjects: T Technology > T Technology (General)
G Geography. Anthropology. Recreation > GA Mathematical geography. Cartography
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: University Structure - Pre August 2011 > School of Engineering Sciences
University Structure - Pre August 2011 > School of Electronics and Computer Science
University Structure - Pre August 2011 > School of Geography > Remote Sensing and Spatial Analysis
ePrint ID: 21974
Date Deposited: 17 Mar 2006
Last Modified: 27 Mar 2014 18:11
URI: http://eprints.soton.ac.uk/id/eprint/21974

Actions (login required)

View Item View Item

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics