Transferability of Artificial Neural Networks for Mapping Land Cover of Regional Areas with High Spatial Resolution Imagery

Accurate and frequently updated land cover maps of environmentally protected areas are necessary for the management of legislation programs governed by the EU, national authorities and local environmental schemes. This study has analysed the suitability of Artificial Neural Networks (ANN) for mapping and monitoring land cover over regional areas, such as National Parks, using both hard and soft classification approaches together with the high spatial resolution of multispectral Carterra Geo IKONOS imagery. The study aimed to examine the transferability of remote sensing mapping algorithms over Northumberland National Park (NNP) located in Northern England. The ANNs were trained using ground data of eight different upland vegetation classes and applied to a multispectral IKONOS image of NNP. The ANNs applied consisted of a Multiple Layer Perceptron (MLP), using a conjugate gradient descent, and one hidden layer with a varying number of hidden nodes and combinations of weights. The transferability of ANNs was found to depend on the ability to generalise, which could be improved by applying early stopping in the training process, improving the accuracy of the validation data by an average of 15%. The classification accuracies for validation pixels of the training areas resulted in 80%, but decreased to less than 50% if evaluated against validation pixels acquired from different areas within NNP. Limitations and issues regarding the transferability of MLP ANNs were observed to be significant. Advanced ANN algorithms such as Support Vector Machines were required to enable the use of ANNs for mapping and monitoring land cover.