Development of a new automated land cover change detection system from remotely sensed imagery based on artificial neural networks

The research is designed to develop and implement the algorithms for an automated spatial change information extraction system from remotely sensed imagery based on artificial neural networks. First, the authors investigate the suitability of the application of neural networks in automated change detection using TM imagery and its related network design problems unique to change detection. They then develop a neural networks-based change detection system using backpropagation training algorithm. This trained network is then able to efficiently detect land cover changes and provide complete information about the nature of change. Based on their experiments, it has been proven that this technique is successful and has immense implications on land cover change detection and quantification at all levels of applications ranging from local to global in scale.