Multi-scale Features and Markov Random Field Model for Powerline Scene Classification

doi:10.11947/j.AGCS.2018.20170556

Abstract

Abstract: Timely and accurate monitoring the safety of power line can prevent dangerous situations effectively. It is proposed that a Markov random field(MRF) model, into which a random forest classifier being integrated, to classify airborne LiDAR point cloud for power line scene. First, it is extracted that multi-scale visual features according to spatial pyramid theory to represent geometry information of the point and its neighborhood. And then a random forest classifier is used to describe the probability distribution of observed data. Meanwhile, contextual prior probability is established using MRF model, which is formulated as a multi-label energy function. Finally, the multi-label graph-cut technique is used to minimize energy function for optimizing the labels. It is validated the proposed method with LiDAR point cloud acquired by helicopter and mini-UAV power line inspection system. Experimental results demonstrate that the model can effectively classify pylon, power line and vegetation, with the overall accuracy of over 98%. Moreover, compared with other methods, the proposed model shows higher classification accuracy, particularly for the classification of the pylon.

Key words: random forest, point cloud classification, multi-scale features, Markov random field, prior knowledge

CLC Number:

P237

YANG Juntao, KANG Zhizhong. Multi-scale Features and Markov Random Field Model for Powerline Scene Classification[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(2): 188-197.

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