We propose in this paper the investigation of the change detection approaches based on the pixel level and the object level. The pixel level approach is based on the simultaneous analysis of multitemporal data, while the object level approach uses a comparative analysis of independently produced classifications of data. Thereby, the comparison is established by using the multilayer neural network classifier. Usually, the backpropagation algorithm is used as a training rule. In this paper, we investigate the use of the Kalman filtering (KF) as the training algorithm for detecting changes in remotely sensed imagery. By using SPOT images and evaluation criteria, the detailed comparison indicates that the KF algorithm is preferable compared to the BP algorithm in terms of convergence rate, stability and change detection accuracy.