Field road segmentation network based on PraNet

IF 1 4区地球科学 Q4 GEOGRAPHY, PHYSICAL

Journal of Spatial Science Pub Date : 2022-04-11 DOI:10.1080/14498596.2022.2059023

Guoqi Liu, Manqi Zhao, Lu Bai, Hecang Zang, Baofang Chang

引用次数: 0

Abstract

ABSTRACT Nowdays, many methods based on CNN have been proposed for road extraction. However, there are still great challenges. Therefore, according to image characteristics, this paper made corresponding improvements based on medical segmentation network PraNet. First, the reverse attention module (RA) connected at the last layer of PraNet is changed to the positive attention module (PA). Then, the negative matrix L1 norm regularization is added into the loss function. We conducted experiments on a data set made by UAV in the field of Henan Academy of Agricultural Sciences. The results show that the proposed method is better than the comparison models.

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基于PraNet的现场道路分割网络

目前，人们提出了许多基于CNN的道路提取方法。然而，仍然存在巨大的挑战。因此，本文根据图像的特点，基于医学分割网络PraNet进行了相应的改进。首先，将连接在PraNet最后一层的反向注意模块(RA)改为正向注意模块(PA)。然后，在损失函数中加入负矩阵L1范数正则化。我们在河南省农业科学院的田野上对无人机采集的数据集进行了实验。结果表明，所提方法优于对比模型。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Spatial Science 地学-地质学

CiteScore

5.00

自引率

5.30%

发文量

审稿时长

>12 weeks

期刊介绍： The Journal of Spatial Science publishes papers broadly across the spatial sciences including such areas as cartography, geodesy, geographic information science, hydrography, digital image analysis and photogrammetry, remote sensing, surveying and related areas. Two types of papers are published by he journal: Research Papers and Professional Papers. Research Papers (including reviews) are peer-reviewed and must meet a minimum standard of making a contribution to the knowledge base of an area of the spatial sciences. This can be achieved through the empirical or theoretical contribution to knowledge that produces significant new outcomes. It is anticipated that Professional Papers will be written by industry practitioners. Professional Papers describe innovative aspects of professional practise and applications that advance the development of the spatial industry.