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基于深度学习的航拍图像道路提取算法

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针对已有道路提取算法存在的道路断裂、噪声等问题,提出了一种"预处理-神经网络推断-后处理"三段式航拍图像道路提取算法.首先,根据航拍图像特点,利用图像增强对其进行预处理,最大限度消除环境因素对图像的影响;然后,设计基于深度学习的推断网络,从预处理后的图像提取道路信息;最后,提出基于形态学的后处理算法,进一步修正道路信息.实验结果表明,所提算法相比于已有算法具有更高的性能.
An aerial-image-oriented road extraction algorithm based on deep learning
A three-stage"preprocessing-inference based on neural network-post-processing"road extraction algorithm is pro-posed to deal with the existing problems of road extraction algorithms,such as road breakage,noise and other problems.Firstly,ac-cording to the characteristics of aerial images,image enhancement is used to maximally eliminate the impact of environmental fac-tors on the images.Then,a deep learning-based inference network is designed to extract road information from the preprocessed im-ages.Finally,a morphology-based post-processing algorithm is proposed to further correct the road information.The experimental results show that the proposed algorithm has higher performance compared to the existing algorithms.

road extraction algorithmaerial imageimage enhancementdeep learningmorphology

郑宇翔、刘信湧、林宇昂、何念

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福州大学梅努斯国际工程学院,福州 350108

厦门大学信息学院,厦门 361005

星宸科技股份有限公司,厦门 361100

福州大学至诚学院,福州 350002

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道路提取算法 航拍图像 图像增强 深度学习 形态学

2024

现代计算机
中大控股

现代计算机

影响因子:0.292
ISSN:1007-1423
年,卷(期):2024.30(21)