Multilayer feedforward networks are universal approximators
Neural Networks
Proceedings of the 20th workshop of the Austrian Association for Pattern Recognition (OAGM/AAPR) on Pattern recognition 1996
Increasing the accuracy of neural network classification using refined training data
Environmental Modelling & Software
| Hi-index | 0.00 |
Landsat Thematic Mapper (TM) imagery can be used to classify different land cover types based on reflectance and emittance characteristics in seven wavelength bands. Various methods, including NDVI and other simple mathematical transformations, can be used to show strong variations in band intensity ratios from different surfaces. However, the number of land cover classes used is commonly low, preventing a detailed mapping of the region of interest. A neural network trained with the backpropagation method should be able to improve on these simple mathematical calculations by developing complex functions which allow recognition of different land cover or land use types. Landsat imagery of Aberdeen and the surrounding area was used to develop a land cover map highlighting areas of residential, commercial and industrial land use, along with various natural and semi-natural land cover classes. Confusion between specific classes is highlighted by the use of a Kohonen self-organizing map to categorize the Landsat multispectral imagery, resulting in a description of the land cover categories that can actually be distinguished from one another using Landsat TM imagery.