SPIE Journal Paper | 1 July 1997
KEYWORDS: Fuzzy logic, Neural networks, Image classification, Vegetation, Neurons, Fuzzy systems, Satellite imaging, Earth observing sensors, Satellites, Visible radiation
A spectral classification comparison was performed using four different classifiers, the parametric maximum likelihood classifier and three nonparametric classifiers: neural networks, fuzzy rules, and fuzzy neural networks. The input image data is a System Pour l'Observation de la Terre (SPOT) satellite image of Otago Harbour near Dunedin, New Zealand. The SPOT image data contains three spectral bands in the green, red, and visible infrared portions of the electromagnetic spectrum. The specific area contains intertidal vegetation species above and below the waterline. Of specific interest is eelgrass (Zostera novazelandica), which is a biotic indicator of environmental health. The mixed covertypes observed in an in situ field survey are difficult to classify because of subjectivity and water's preferential absorption of the visible infrared spectrum. In this analysis, each of the classifiers were applied to the data in two different testing procedures. In the first test procedure, the reference data was divided into training and test by area. Although this is an efficient data handling technique, the classifier is not presented with all of the subtle microclimate variations. In the second test procedure, the same reference areas were amalgamated and randomly sorted into training and test data. The amalgamation and sorting were performed external to the analysis software. For the first testing procedure, the highest testing accuracy was obtained through the use of fuzzy inferences at 89%. In the second testing procedure, the maximum likelihood classifier and the fuzzy neural networks provided the best results. Although the testing accuracy for the maximum likelihood classifier and the fuzzy neural networks were similar, the latter algorithm has additional features, such as rules extraction, explanation, and fine tuning of individual classes.
