Change detection from multi-temporal remote sensing images byintegrating multiple features
融合多特征的遥感影像变化检测

Traditional change detection approaches from multi-temporal remote sensing images are mainly based on spectralinformation in original images, without utilizing other derived features, such as texture, geometrical structure and shape. With theincreasing spatial resolution in remote sensing imagery, change detection only relying on spectral information cannot guaranteethe completeness and accuracy of change targets, suggesting the importance to integrate the merits of different features. Afterextracting multiple features from original images, two change detection procedures based on information fusion strategies areproposed in this paper:weighted similarity distance in one-dimensional feature space, and fuzzy set theory and support vectormachines in n-dimensional feature space, respectively. Multi-temporal QuickBird high-resolution images are used as experimentaldata 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 bewell preserved to highlight the important change targets at the same time.