Comparative performance of neural networks and maximum likelihood for supervised classification of agricultural crops: single date and temporal analysis

Maximum likelihood, backpropagation and radial basis neural networks were applied in the supervised classification of agricultural crops. Ten ETM+t/Landsat rectified images in bands 3, 4, 5 and NDVI were used as input data for the classification. The NDVI input was used as an indicator for changes in the leaf area index and, by correlation, the phenological cycle. Agriculture in the study area makes the spectral characterization of dry season crops troublesome since irrigation possibilities give the planting date flexibility, while the phenological stages in training polygons are rarely representative of the whole image. Kappa statistics showed that temporal classification, which analyses a pixel in continuum, improved the discrimination in comparison to a single spectral date at a significant level (p < 0.05) in many dates. The neural network models (multilayer perceptron and radial basis functions) had a very similar performance that surpassed the maximum likelihood method.