Change Detection Using Neural Network with Improvement Factor in Satellite Images

Abstract

Problem statement: The aim of this study is to investigate the applicability of using the neural network techniques in change detection of remotely sensed data. Approach: In addition, the tuning parameters of the network, such as encoding the output classes, adding the momentum term and learning rate, are investigated in order to achieve best network performance. Results: Neural network-based change detection system in this study is implemented using back propagation-training algorithm. This trained network is designed to be able to detect efficiently any variation between two images and provide adequate information about the type of changes. In an effort to meet these requirements, neural network scheme with improvement factor, leaning rate and momentum term is proposed to monitor environmental changes in Toshka area, Egypt. Two sets of satellite images with different dates are used, the first set contains of two sample satellite images, the second set of images acquired on 1984, 2000 and 2003. Conclusion/Recommendations: Comparing the output of the proposed model with the mostly used change detection techniques; ratio and classification, results show a great potential as the proposed scheme was able to identify not only the changed and non-changed area but also it was capable to identify the nature of these changes.