A Probabilistic Framework for Building Extraction From Airborne Color Image and DSM

作者：Chai, Dengfeng^*

来源：IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(3): 948-959.

DOI：10.1109/JSTARS.2016.2616446

• Buildings
• hidden Markov models
• image segmentation
• maximum a posteriori (MAP) estimation

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 pixellabeling 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. Thesemodels facilitate parameter learning andmodel 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.

• 出版日期2017-3
• 单位浙江大学