常规多时相遥感影像变化检测主要基于光谱信息,没有充分利用纹理、几何、形状等多种特征信息,不足以体现检测目标的完整性和准确性.本文针对不同特征在变化检测中应用的优势,在提取影像多种特征的基础上,构建了1维和多维两种基于信息融合策略的变化检测方法,即利用1维特征空间加权距离相似度运算、多维特征空间的模糊集融合和支持向量机融合策略进行变化检测.利用多时相QuickBird高分辨率遥感影像进行城市土地覆盖变化检测试验,结果表明,本文方法可以有效集成不同特征的优势与表征变化信息的能力,提高变化检测过程的稳定性和适用性,同时能够更好地保持变化地物的结构和形状,突出主要变化目标.%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.
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