Performance of a neural network: mapping forests using GIS and remotely sensed data

It is concluded that the neuralnetwork approach does not offer significant advantages over conventional classification schemes for mapping eucalypt forests from Landsat TM and ancillary GIs data at the Anderson Level 111 forest type level.

Abstract

Neural networks have been proposed to classify remotely sensed and ancillary CIS data. In this paper, the backpropagation algorithm is critically evaluated, using as an example, the mapping of a eucalypt forest on the far south coast of New South Wales, Australia. A GIS database was combined with Landsat thematic mapper data, and 190 plots were field sampled in order to train the neural network model and to evaluate the resulting classifications. The results show that the neural network did not accurately classify GIS and remotely sensed data at the forest type level (Anderson Level III), though conventional classifiers also perjGorm poorly with this type of problem. Previous studies using neural networks have classified more general (e.g., Anderson Level I, II) landcover types at a higher accuracy than those obtained here, but mapped land cover into more general themes. Given the poor classification results and the difficulties associated with the setting up of suitable parameters for the neural-network (backpropagation) algorithm, it is concluded that the neuralnetwork approach does not offer significant advantages over conventional classification schemes for mapping eucalypt forests from Landsat TM and ancillary GIs data at the Anderson Level 111 forest type level.