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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >A Probabilistic Framework for Building Extraction From Airborne Color Image and DSM
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A Probabilistic Framework for Building Extraction From Airborne Color Image and DSM

机译：从机载彩色图像和DSM中提取建筑物的概率框架

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This paper proposes a supervised probabilistic framework for building extraction. Multiple features from both images and point clouds can be fused in this framework to achieve global optimal building extraction. Basically, it adopts conditional random fields (CRFs) to discriminatively model the ground scene and its observed data, and formulates building extraction as a pixel-labeling problem. Color, edge, and height explored from both kinds of data are fused into the association potential and interaction potential of CRFs in order to achieve a global optimal labeling. Furthermore, it develops Gaussian mixture model and height range model for color distribution and height distribution, respectively. These models facilitate parameter learning and model specification from training data. Furthermore, it constructs regular interaction potentials such that global optimization can be solved efficiently by a graph cut algorithm. With the proposed framework, buildings can be extracted automatically and efficiently if training data are annotated in advance. As demonstrated by the experiments and their evaluations, the proposed approach outperforms state of the art techniques and allows further improvement by incorporating additional data or features.
机译：本文提出了一种用于建筑物提取的监督概率框架。来自图像和点云的多个特征可以在此框架中融合，以实现全局最佳建筑物提取。基本上，它采用条件随机场（CRF）来区分地面场景及其观测数据的模型，并将建筑物提取公式化为像素标记问题。从两种数据中探究的颜色，边缘和高度都融合到CRF的关联电位和相互作用电位中，以实现全局最佳标记。此外，它还分别开发了用于颜色分布和高度分布的高斯混合模型和高度范围模型。这些模型有助于根据训练数据进行参数学习和模型说明。此外，它构建了规则的交互电位，从而可以通过图割算法有效地解决全局优化问题。利用所提出的框架，如果预先注释训练数据，则可以自动且有效地提取建筑物。正如实验及其评估所证明的那样，所提出的方法优于现有技术，并且可以通过合并其他数据或功能进一步改进。

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