Continuum removal (CR) is often used for geologic mapping; however, more research is needed to better establish the utility of CR for vegetation classification, particularly when used with artificial neural networks (ANNs). In this paper, fuzzy learning vector quantization (FLVQ) was applied to hyperspectral Airborne Visible/Infrared Imaging Spectrometer imagery for coastal vegetation classification. FLVQ performance was compared with that of a multilayer perceptron (MLP), a self-organizing map (SOM), and an endmember-based algorithm [spectral feature fitting (SFF)]. The objective was to assess the effect of CR as an input vector-preprocessing step for ANN model development on classification accuracy. Compared with a related study, continuum intact (CI) reflectance data generally yielded higher classification accuracies than those based on CR. Thus, CR may not be a preferred preprocessing method for coastal vegetation mapping over broad wavelength ranges. MLP slightly outperformed FLVQ when applied to CI data, but FLVQ yielded higher accuracy than MLP with CR. However, there was no significant difference between them for both data treatments at the 95% confidence level. All ANNs tested yielded significantly higher classification accuracies than SFF. For model development, the 588-neuron FLVQ required only 8.2% of MLP training time, 27.8% of the 400-neuron SOM time, and 8.8% of the 729-neuron 3-D SOM time
['Anthony M. Filippi', 'John R. Jensen']
Effect of Continuum Removal on Hyperspectral Coastal Vegetation Classification Using a Fuzzy Learning Vector Quantizer