基于深度学习模型的复杂困难路网提取方法

A method for extracting complex and difficult road networks based on deep learning models

针对复杂困难地区公路信息提取中存在的数据获取困难、道路特征不统一、自然环境干扰、道路变化频繁等难点问题,本文应用高分遥感技术,开展了复杂困难地区路网信息的提取、处理研究。基于深度学习模型不断优化的路网提取方法,以高分卫星为主要数据源,通过对复杂困难地区不同类型农村公路的光谱、纹理及可分离性特征集等进行学习,识别出等级低和道路条件差的农村公路,为农村路网识别与判别提供准确的数据保障;及时获取客观、准确和全面的农村公路基础数据解决了复杂困难地区路网信息不足的问题;为复杂困难地区公路网的规划、建设提供科学的决策依据,为出行交通诱导提供了便利。

With the wide spread application of high-resolution satellite image remote sensing technology,it also plays a huge driving role in transportation.There are technical difficulties in extracting rural road networks,such as data acquisition difficulties,inconsistent road features,natural environment interference,and frequent road changes.This study mainly uses high-resolution satellites as the main data source,and based on deep learning algorithms,learns the spectral,texture,and separable feature sets of different types of rural roads to identify rural roads with low grades and poor road conditions,which provides accurate data guarantee for the identification and discrimination of rural road networks,timely obtaining objective,accurate,and comprehensive basic data of rural roads solves the problem of insufficient road network information in complex and difficult areas.This study can serve scientific decision-making basis for the planning and construction of road networks in complex and difficult areas,and facilitates travel and traffic guidance.