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As a result of urbanization, land use/land cover classes in urban areas are changing rapidly, and this trend increased in the recent years. Change information detected from multi-temporal remote sensing images can thus help to understand urban development and to effectively support urban planning. Differences in reflectance spectra, easily obtained by multi-temporal remote sensing images, are important indicators to characterize these changes. Although many algorithms were proposed to generate difference images, the results are usually greatly inconsistent. In order to integrate the merits of different algorithms to recognize spectral changes, fusion techniques merging multiple difference images are proposed and implemented in this paper. Feature and decision level fusion are used to combine simple change detectors, and to build an automatic change detection procedure. The proposed approach is tested with multi-temporal CBERS and HJ-1 images, and experimental results demonstrate its effectiveness and reliability. By integrating different change information, the appropriate fusion method can be selected according to the specific application in order to minimize the omission or the commission errors.