Performance evaluation of statistical and neural network classifiers for automatic land use/cover classification

A comparative analysis of the performance of five classifier types combined with four feature extraction techniques is presented for the automatic recognition of land use/cover categories from aerial imagery through texture analysis. The classification accuracy of the linear, Bayes quadratic, k-nearest neighbor, Parzen, and backpropagation-trained multi-layer perceptron classifiers are evaluated in combination with the following texture measures: spatial gray-level co-occurrence matrix, Laws, Liu-Jernigan, and Fourier domain rings and wedges. Examples of four land use/cover classes -- urban, fields, trees, and water -- are manually delineated from commercial aerial survey panchromatic images per the U.S. Geological Survey Land Use/Land Cover Classification System. Through leave-one-scene-out sampling, each classifier type is trained and tested using feature vectors generated by each feature extraction technique. Mean classification error and an 80% confidence interval for each combination of classifier- feature extraction method is determined. Error overlap is analyzed to assess improvement of performance though fusing the results from two or more classifier-feature set combinations. The significance of this work likes both in the results of the comparative analysis and in its adherence to formal experimental methodology. We anticipate that these results will be applicable to a wide variety of image recognition problems where texture is a principle discriminant, including medical screening, remote sensing, and materials identification.