HIGH QUALITY FACADE SEGMENTATION BASED ON STRUCTURED RANDOM FOREST, REGION PROPOSAL NETWORK AND RECTANGULAR FITTING

HIGH QUALITY FACADE SEGMENTATION BASED ON STRUCTURED RANDOM FOREST, REGION PROPOSAL NETWORK AND RECTANGULAR FITTING
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DOI：
10.5194/isprs-annals-iv-2-223-2018
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发表时间：
2018-05
期刊：
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
影响因子：
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通讯作者：
Kujtim Rahmani;H. Mayer
Kujtim Rahmani;H. Mayer
中科院分区：
其他
文献类型：
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作者：
Kujtim Rahmani;H. Mayer

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摘要。在本文中，我们提出了一个利用结构化随机森林（SRF）、基于卷积神经网络（CNN）的区域提议网络（RPN）以及矩形拟合优化对建筑外立面进行高质量语义分割的流程。我们的主要贡献在于，我们将由RPN创建的特征用作SRF中的通道。我们通过实验表明，这对于门窗尤其有效。我们的流程在两个数据集上进行了评估，结果优于当前最先进的方法。此外，我们还量化了RPN和矩形拟合优化对结果准确性的贡献。
Abstract. In this paper we present a pipeline for high quality semantic segmentation of building facades using Structured Random Forest (SRF), Region Proposal Network (RPN) based on a Convolutional Neural Network (CNN) as well as rectangular fitting optimization. Our main contribution is that we employ features created by the RPN as channels in the SRF.We empirically show that this is very effective especially for doors and windows. Our pipeline is evaluated on two datasets where we outperform current state-of-the-art methods. Additionally, we quantify the contribution of the RPN and the rectangular fitting optimization on the accuracy of the result.