Change detection using adaptive fuzzy neural networks: Environmental damage assessment after the Gulf War

This article introduces an adaptive fuzzy neural network classifier for environmental change detection and classification applied to monitor landcover changes resulting from the Gulf War. In this study, landcover change is treated as a qualitative shift between landcover categories. The Change Detection Adaptive Fuzzy (CDAF) network learns fuzzy membership functions for each landcover class present at the first image date based on a sample of the image data. An image from a later date is then classified using this network to recognize change among familiar classes as well as change to unfamiliar landcover classes. The CDAF network predicts landcover change with 86% accuracy representing an improvement over both a standard multidate K-means technique which performed at 70% accuracy and a hybrid approach using a maximum likelihood classifier (MLC)/K-means which achieved 65% accuracy. In this study, we developed a hybrid classifier based on conventional statistical methods (MLC/K-means classifier) for comparison purposes to help evaluate the performance of the CDAF network. The CDAF compared with existing change detection methodology has two features that lead to significant performance improvements: 1) new landcover types created by a change event automatically lead to the establishment of new landcover categories through an unsupervised learning strategy, and 2) for each pixel the distribution of fuzzy membership values across possible categories are compared to determine whether a significant change has occurred.