结合融合策略的光学影像道路提取技术  

Fusion-enhanced optical image road extraction technique

作　　者：王淑香[1] 林雨准 金飞[1] 杨小兵 黄子恒 程传祥 WANG Shuxiang;LIN Yuzhun;JIN Fei;YANG Xiaobing;HUANG Ziheng;CHENG Chuanxiang(Information Engineering University,Zhengzhou 450001,China;Troops 32020,Wuhan 430000,China)

机构地区：[1]信息工程大学,河南郑州450001 [2]32020部队,湖北武汉430000

出　　处：《测绘通报》2024年第4期6-12,共7页Bulletin of Surveying and Mapping

摘　　要：成像系统获取数据时一般无法兼顾空间和光谱信息,但当前的光学影像道路提取往往直接以融合后影像为数据源,聚焦网络结构、监督形式等方面的研究,未对融合效果在道路提取中的作用进行深入探索与分析。因此,本文提出了一种结合融合策略的光学影像道路提取技术。首先,以端到端的“编码—解码”网络为基本结构,并结合输入数据的类别、数量等因素进行针对性改进与设计,为后续的试验验证提供训练和测试框架;然后,立足空间信息和光谱信息的注入偏好,选取4种典型的影像融合方法,并以此为技术支持对全色影像和多光谱影像进行融合;最后,在试验部分借助2个公开数据进行了集验证,得出融合策略在道路提取中可有效提升量化评价指标的结论,同时对典型的道路重难点区域提取具有积极的正向促进作用。Imaging systems often face challenges in simultaneously considering both spatial and spectral information.However,existing optical image road extraction methods primarily rely on fused images as data sources,with a focus on aspects such as network structures and supervision forms,without thoroughly exploring and analyzing the impact of fusion effects on road extraction.This paper proposes an optical image road extraction technique that incorporates fusion strategies.Firstly,an“encoder-decoder”network is adopted as the fundamental structure,and targeted improvements and designs are made based on factors such as the categories and quantities of input data,providing a training and testing framework for subsequent experimental verification.Secondly,to favor the injection of spatial and spectral information,four representative image fusion methods are selected.These methods are utilized to fuse panchromatic and multispectral images,providing the necessary technical support.Finally,the experimental section employs two publicly available datasets to demonstrate the effectiveness of the fusion strategy in improving quantitative evaluation metrics for road extraction.Furthermore,the fusion strategy demonstrates a positive and facilitating effect on the extraction of road areas within challenging regions.

关 键 词：影像融合 全色影像 多光谱影像 道路提取 卷积神经网络 

分 类 号：P237[天文地球—摄影测量与遥感]