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Abstract
change detection is the process of identifying differences in the state of an object or phenomenon by observing it at different time. essentially, it involves the ability to quantify temporal effects using multi-temporal data sets.
aim of this work to investigate the applicability of using the neural network techniques in change detection of remotely sesed data. in addition the tuning parameters of the nerwork, such as encoding the output classes, adding the momentum term, and learning rate, are investigated in order to achieve bast network performance.change detection is the process of identifing differences in the state of an object or phenomenon by observing it at different timees. neural network-based change dation system in thisd study is implemented using back propagation-trauning algorithm. this trained network is desgned to be able to detect efficiently any variation between two images and provide adequate information about the type of changes.