Remotely Sensed Change Detection Based on Artificial Neural Networks

A new method for remotely sensed change detection based on artificial neural networks is presented. The algorithm for an automated land-cover change-detection system was developed and implemented based on the current neural network tech-niques for multispectral image classification. The suitability of application of neural networks in change detection and its related network design considerations unique to change detection were first investigated. A neural-network-based change-detection system using the backpropagation training algorithm was then developed. The trained four-layered neural network was able to provide complete categorical information about the nature of changes and detect land-cover changes with an overall accuracy of 95.6 percent for a four-class (i.e., 16 change classes) classification scheme. Using the same training data, a maximum-likelihood supervised classification produced an accuracy of 86.5 percent. The experimental results using multitemporal Landsat Thematic Mapper imagery of Wilmington, North Carolina are provided. Findings of this study demonstrated the potential and advantages of using neural network in multitemporal change analysis