高分辨率遥感影像场景变化检测的相似度方法北大核心CSCD

针对传统高分辨率遥感影像场景变化检测流程复杂且严重依赖分类结果的问题,本文提出了一种顾及场景全局与局部相似性的变化检测方法。首先,将同一区域两个时相的遥感影像裁切成固定尺寸的图像块,构造场景对图像库,并划分为训练集和测试集;其次,构建融合场景全局与局部相似性的双分支卷积神经网络,实现场景相似度学习;然后,利用训练的相似度学习网络提取训练集场景相似度,并通过阈值遍历的方法得到最佳的相似度阈值;最后,基于相似度阈值将测试集场景对划分为变化场景和未变化场景,得到最终的变化检测结果。试验结果表明,本文方法的总体精度为0.94,Kappa系数为0.88,优于传统的分类后变化检测方法,是一种简单有效的场景变化检测方法。

Similarity method for high-resolution remote sensing scene change detection

To solve the problem that the process of traditional remote sensing image scene change detection is complex and severely depends on the classification performance, a scene change detection method considering both global and local scene similarity is proposed in this paper. Firstly, the two temporal remote sensing images are cropped into image patches with fixed size to construct scene pair database, and divided into training and testing dataset. Secondly, the two-branch convolutional neural network integrating scene global and local similarity is constructed to carry out similarity learning of scene pairs. Then, the trained network is used to extract the similarity of scene pairs of the training dataset, and the best similarity threshold is determined via traversal. Finally, the testing dataset is categorized into changed and unchanged scenes based on the similarity threshold to obtain the final change detection results. The experimental results show that the overall accuracy and Kappa of the proposed method is 0.94 and 0.88, respectively, which outperforms traditional detection after classification approach and is a simple yet effective change detection method.