In the past few years, graph-based methods have proven to be a useful tool in a wide variety of energy minimization problems. In this article, we propose a graph-based algorithm for feature extraction and segmentation of multimodal images. By defining a notion of similarity that integrates information from each modality, we create a fused graph that merges the different data sources. The graph Laplacian then allows us to perform feature extraction and segmentation on the fused data set. We apply this method in a practical example, namely, the segmentation of optical and LiDAR images. The results obtained confirm the potential of the proposed method.

中文翻译：

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基于图的多模态图像数据融合和分割方法

在过去几年中，基于图的方法已被证明是解决各种能量最小化问题的有用工具。在本文中，我们提出了一种基于图的算法，用于多模态图像的特征提取和分割。通过定义集成来自每种模态的信息的相似性概念，我们创建了一个融合不同数据源的融合图。然后，图拉普拉斯算子允许我们对融合数据集执行特征提取和分割。我们将这种方法应用在一个实际例子中，即光学和 LiDAR 图像的分割。获得的结果证实了所提出方法的潜力。