In this letter, a novel method for change detection is proposed using neighborhood structure correlation. Because structure features are insensitive to the intensity differences between bi-temporal images, we perform the correlation analysis on structure features rather than intensity information. First, we extract the structure feature maps by using multi-orientated gradient information. Then, the structure feature maps are used to obtain the Neighborhood Structural Correlation Image (NSCI), which can represent the context structure information. In addition, we introduce a measure named matching error which can be used to improve neighborhood information. Subsequently, a change detection model based on the random forest is constructed. The NSCI feature and matching error are used as the model inputs for training and prediction. Finally, the decision tree voting is used to produce the change detection result. To evaluate the performance of the proposed method, it was compared with three state-of-the-art change detection methods. The experimental results on two datasets demonstrated the effectiveness and robustness of the proposed method.

翻译：本论文提出一种基于邻域结构相关性的新型变化检测方法。由于结构特征对双时相图像间的强度差异不敏感，因此本文针对结构特征而非强度信息进行相关性分析。首先，通过多方向梯度信息提取结构特征图；随后利用结构特征图生成可表征上下文结构信息的邻域结构相关图像(NSCI)。此外，引入名为匹配误差的度量指标以改进邻域信息。进而构建基于随机森林的变化检测模型，将NSCI特征与匹配误差作为模型输入用于训练与预测，最终通过决策树投票生成变化检测结果。为评估所提方法的性能，将其与三种当前最优的变化检测方法进行对比，在两个数据集上的实验证明了该方法的有效性与鲁棒性。