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Artificial neural networks for mapping regional-scale upland vegetation from high spatial resolution imagery

Lookup NU author(s): Dr Henny Mills, Dr Mark Cutler, Dr David Fairbairn

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Abstract

Upland vegetation represents an important resource that requires frequent monitoring. However, the heterogeneous nature of upland vegetation and lack of ground data require classification techniques that have a high degree of generalization ability. This study investigates the use of artificial neural networks as a means of mapping upland vegetation from remotely sensed data. First, the optimum size of support to map upland vegetation was estimated as being less than 4 m, which suggested that soft classification techniques and high spatial resolution IKONOS imagery were required. The use of high spatial resolution imagery for regional-scale areas has introduced new challenges to the remote sensing community, such as using limited ground data and mapping land-cover dynamics and variation over large areas. This work then investigated the utility of artificial neural networks (ANN) for regional-scale upland vegetation from IKONOS imagery using limited ground data and to map unseen data from remote geographical locations. A Multiple Layer Perceptron was trained with pixels from an IKONOS image using early stopping; however, despite high classification accuracies when calculated for pixels from an area where training pixels were extracted, the networks did not produce high accuracies when applied to unseen data from a remote area.


Publication metadata

Author(s): Mills H, Cutler MEJ, Fairbairn D

Publication type: Article

Publication status: Published

Journal: International Journal of Remote Sensing

Year: 2006

Volume: 27

Issue: 11

Pages: 2177-2195

Print publication date: 01/06/2006

ISSN (print): 0143-1161

ISSN (electronic): 1366-5901

Publisher: Taylor & Francis Ltd.

URL: http://dx.doi.org/10.1080/01431160500396501

DOI: 10.1080/01431160500396501


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