Traditional change detection approaches from multi-temporal remote sensing images are mainly based on spectral information in original images

without utilizing other derived features

such as texture

geometrical structure and shape.With the increasing spatial resolution in remote sensing imagery

change detection only relying on spectral information cannot guarantee the completeness and accuracy of change targets

suggesting the importance to integrate the merits of different features.After extracting multiple features from original images

two change detection procedures based on information fusion strategies are proposed in this paper:weighted similarity distance in one-dimensional feature space

and fuzzy set theory and support vector machines in n-dimensional feature space

respectively.Multi-temporal QuickBird high-resolution images are used as experimental data for land cover change detection over urban areas

and the results demonstrate the effectiveness of the proposed method.By integrating the merits of different features

the stability and applicability can be improved

and the structure and shape can be well preserved to highlight the important change targets at the same time.