ABSTRACT In this paper, in order to improve the accuracy of multi-source remote sensing image building change detection, we propose a method based on multi-feature fusion (MFF) and extreme learning machine (ELM) training. Firstly, we fuse the spectral feature difference image (DI) and textural feature (grey level co-occurrence matrix) DI obtained by change vector analysis (CVA), morphological building index DI, and shape feature DI obtained by subtraction to construct the final DI. Secondly, the coarse change detection map obtained by selecting a threshold for the DI saliency map obtained by the use of the frequency-domain significance (FDS) method is pre-classified by the fuzzy c-means (FCM) clustering algorithm. Finally, the neighborhood features obtained from the original images and the feature images of the changed pixels (buildings) and the unchanged pixels in the coarse change map are extracted and used as reliable samples for the ELM training. By using the trained ELM classifier, undetermined pixels are further separated into changed and unchanged classes. Finally, we combine the ELM classification result and the preclassification result together to form the final building change map. Experiments on two real multi-source datasets show that the proposed method can result in a significant improvement in multi-source remote sensing image building change detection performance.

中文翻译：

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基于多特征融合和极限学习机的多源遥感影像建筑变化检测

摘要在本文中，为了提高多源遥感影像建筑物变化检测的准确性，我们提出了一种基于多特征融合（MFF）和极限学习机（ELM）训练的方法。首先，我们将变化向量分析（CVA）得到的光谱特征差分图像（DI）和纹理特征（灰度共生矩阵）DI、形态构建指数DI和减法得到的形状特征DI融合，构建最终的DI . 其次，对使用频域显着性(FDS)方法得到的DI显着性图选择阈值得到的粗变化检测图，通过模糊c-均值(FCM)聚类算法进行预分类。最后，从原始图像中获得的邻域特征以及粗变图中变化像素（建筑物）和不变像素的特征图像被提取出来，作为ELM训练的可靠样本。通过使用经过训练的 ELM 分类器，未确定的像素被进一步分为变化的和不变的类别。最后，我们将 ELM 分类结果和预分类结果结合在一起，形成最终的建筑变化图。在两个真实的多源数据集上的实验表明，所提出的方法可以显着提高多源遥感影像建筑物变化检测性能。未确定的像素进一步分为变化的和不变的类别。最后，我们将 ELM 分类结果和预分类结果结合在一起，形成最终的建筑变化图。在两个真实的多源数据集上的实验表明，所提出的方法可以显着提高多源遥感影像建筑物变化检测性能。未确定的像素进一步分为变化的和不变的类别。最后，我们将 ELM 分类结果和预分类结果结合在一起，形成最终的建筑变化图。在两个真实的多源数据集上的实验表明，所提出的方法可以显着提高多源遥感影像建筑物变化检测性能。