Fast object-level change detection for moving VHR images

A novel approach is presented for change detection of very high resolution images, which is accomplished by fast object level change feature extraction and progressive change feature classification. In addition, the Kalman filter used to track VHR images. Object-level change feature is helpful for improving the discriminability between the changed class and the unchanged class. Progressive change feature classification helps to improve the accuracy and the degree of automation, which is implemented by dynamically adjusting the training samples and gradually tuning the separating hyper plane. Experiments demonstrate the effectiveness and accuracy of the proposed approach. The simulation and experiment results of the model indicate that the algorithm efficiently solves the problem of nonlinear moving object Tracking.