Improving Artificial Neural Networks Using Texture Analysis and Decision Trees for the Classification of Land Cover

Abstract

The purpose of this research was to improve artificial neural network (ANN) classification of land cover using texture analysis and decision trees. Three variants on ANN-based classifiers were applied to Landsat-7 data of southwestern Ohio for an Anderson Level-II land-cover classification: (1) the use of a customized architecture for each land-cover class; (2) the use of texture analysis for urban classes; and (3) the use of a decision tree (DT) classifier to refine the ANN output. An accuracy assessment was performed on the final ANN classification and compared to the USGS National Land Cover Data (NLCD). Overall accuracy of the ANN was 85%, compared to 69% for the NLCD. Producer accuracies for the ANN ranged from 64% to 96% compared to 29 to 86% for the NLCD. User accuracies for the ANN ranged from 71 to 99% compared to 36-87% for the NLCD. The ANN methodology will be used to classify the state of Ohio and could potentially be used on a national scale.