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首页> 外文会议>IEEE Conference on Computer Vision and Pattern Recognition Workshops >Dense Semantic Labeling of Very-High-Resolution Aerial Imagery and LiDAR with Fully-Convolutional Neural Networks and Higher-Order CRFs
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Dense Semantic Labeling of Very-High-Resolution Aerial Imagery and LiDAR with Fully-Convolutional Neural Networks and Higher-Order CRFs

机译：具有全卷积神经网络和高阶CRF的高分辨率空中图像和LIDAR的密集语义标记

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摘要

The increasing availability of very-high-resolution (VHR) aerial optical images as well as coregistered Li-DAR data opens great opportunities for improving object-level dense semantic labeling of airborne remote sensing imagery. As a result, efficient and effective multisensor fusion techniques are needed to fully exploit these complementary data modalities. Recent researches demonstrated how to process remote sensing images using pre-trained deep convolutional neural networks (DCNNs) at the feature level. In this paper, we propose a decision-level fusion approach using a probabilistic graphical model for the task of dense semantic labeling. Our proposed method first obtains two initial probabilistic labeling predictions from a fully-convolutional neural network and a linear classifier, e.g. logistic regression, respectively. These two predictions are then combined within a higher-order conditional random field (CRF). We utilize graph cut inference to estimate the final dense semantic labeling results. Higher-order CRF modeling helps to resolve fusion ambiguities by explicitly using the spatial contextual information, which can be learned from the training data. Experiments on the ISPRS 2D semantic labeling Potsdam dataset show that our proposed approach compares favorably to the state-of-the-art baseline methods.
机译：非常高分辨率（VHR）空中光学图像以及内心的Li-DAR数据的增加，为改善空气遥感图像的物体级密集语义标记而开辟了很大的机会。结果，需要有效且有效的多传感器融合技术来充分利用这些互补数据方式。最近的研究表明如何在特征级别使用预先训练的深卷积神经网络（DCNNS）处理遥感图像。在本文中，我们提出了一种利用概率图形模型来提出决策级融合方法，用于茂密语义标签的任务。我们所提出的方法首先从完全卷积神经网络和线性分类器获得两个初始概率标记预测。物流回归分别。然后在高阶条件随机字段（CRF）内组合这两种预测。我们利用图表削减推理来估计最终密集的语义标记结果。高阶CRF建模有助于通过明确使用空间上下文信息来解决融合歧义，可以从培训数据中学习。 ISPRS 2D语义标签Potsdam数据集的实验表明，我们的建议方法对最先进的基线方法有利地进行了比较。

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