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Change detection based on oriented-object employs objects to show real world. It can reflect visually change of real objects. Result of the method is easier to be understood and re-used. Meanwhile, applying support vector machine (SVM) to change detection can avoid requiring for samples distributing like traditional methods and the questions resulted from over learning like other machine learning methods. And the application can receive higher accuracy. So applying support vector machine along with oriented-object to change detection provides new idea for change detection. By proving, applying support vector machine and oriented-object to change detection supplies facility for result's re-use. And compared to result's readability and precision of other traditional methods, which of this method are higher.