Publication Information

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{'针对目前利用高分遥感数据提取农村道路的研究与应用少,提取结果精准度不够的问题,提出了结合空洞卷积和ASPP(Atrous': [0], 'Spatial': [1, 6], 'Pyramid': [2, 7], 'Pooling)结构的改进全卷积农村道路提取网络模型DC-Net(Dilated': [3], 'Convolution': [4], 'Network)。该模型基于全卷积的编解码结构来提取道路深度特征信息,同时针对农村道路细长的特点,在解编码层之间加入了以空洞卷积为基础的ASPP(Atrous': [5], 'Pooling)结构来提取道路的多尺度特征信息,在不牺牲特征空间分辨率的同时扩大了特征感受野FOV(Field-of-View),从而提高细窄农村道路的识别率。以长株潭城市群郊区部分区域为试验对象,以高分二号国产卫星遥感影像为实验数据,将本文提出的方法与经典的几种全卷积网络方法进行实验结果对比分析。实验结果表明:(1)本文所提出的道路提取模型DC-Net在农村道路的提取上具有可行性,整体提取平均精度达到98.72%,具有较高的提取精度;(2)对比几种经典的全卷积网络模型在农村道路提取上的效果,DC-Net在农村道路提取的精度和连结性、以及树木和阴影的遮挡方面,均表现出了较好的提取结果;(3)本文提出的改进全卷积网络道路提取模型能够有效地提取高分辨率遥感影像中农村道路的特征信息,总体提取效果较好,为提高基于国产高分影像的农村道路提取精度提供了一种新的思路和方法。': [8]}, 'cited_by_api_url': 'https://api.openalex.org/works?filter=cites:W4382897028', 'counts_by_year': [{'year': 2024, 'cited_by_count': 1}, {'year': 2023, 'cited_by_count': 3}, {'year': 2022, 'cited_by_count': 1}], 'updated_date': '2024-12-15T17:51:11.244294', 'created_date': '2023-07-03'}