基于交互式分割技术和决策级融合的SAR图像变化检测

为免去去除斑点噪声的预处理操作及克服选择分布模型的限制，本文结合差异图的特点和一种不涉及分布模型的交互式分割方法，产生不同“种子点”下的变化检测结果后，再利用投票策略对其进行决策级的融合给出最终的变化检测结果。分割中，将每个像素的特征设为差异图及由静态小波变换分解差异图再丢弃高频系数后重构得到的各层表示中，其对应位置上的灰度值构成的矢量。此特征及决策级融合的策略使本文的变化检测技术对SAR图像中的斑点噪声具有一定的鲁棒性。在无需对SAR图像做预处理的情况下，对真实SAR图像数据集的变化检测结果，其效果优于其他相关技术的。

Interactive Segmentation Technique and Decision-level Fusion Based Change Detection for SAR Images

To avoid reducing the speckle noise,and meanwhile overcome the limitation of selecting the distribution model,first,characteristics of the difference image(DI) are integrated with an interactive segmentation method not referring to any distribution assumption,to generate change detection maps corresponding to different "seeds",then a voting competition strategy is used to fuse those results to give the final change detection map.During segmenting,the feature of each pixel is set as a vector consisted of the corresponding intensities in the DI and each scale representation of the DI given by stationary wavelet transform(SWT).This kind of features and the decision level fusion make our proposed method robust to the speckle noise.Results on real SAR datasets obtained under the situation that there is no despeckling preprocessing of SAR images confirm the effectiveness of our method.