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Lookup NU author(s): Dr Henny Mills,
Dr Mark Cutler,
Dr David Fairbairn
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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.
Author(s): Mills H, Cutler MEJ, Fairbairn D
Publication type: Article
Publication status: Published
Journal: International Journal of Remote Sensing
Print publication date: 01/06/2006
ISSN (print): 0143-1161
ISSN (electronic): 1366-5901
Publisher: Taylor & Francis Ltd.
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