基于改进D-LinkNet模型的高分遥感影像道路提取研究  

Research of Road Extraction from High-Resolution Remote Sensing Images Based on Improved D-LinkNet Model

作　　者：张立恒 王浩[1] 薛博维 何立明[1] 吕悦 ZHANG Liheng;WANG Hao;XUE Bowei;HE Liming;LüYue(School of Information Engineering,Chang'an University,Xi'an 710064,China;Xi'an Zhongkexingtu Space Data Technology Co.,Ltd,Xi'an 710000,China)

机构地区：[1]长安大学信息工程学院,西安710064 [2]西安中科星图空间数据技术有限公司,西安710000

出　　处：《计算机工程》2021年第9期288-296,共9页Computer Engineering

基　　金：中央高校基本科研业务费专项资金(300102249302)。

摘　　要：针对高分影像中的道路信息易受植被阴影、高楼建筑物、河流等非道路信息干扰的问题,提出一种高分遥感影像道路提取模型。在中心区域引入channel-spatial双注意力机制捕获道路信息的全局特征依赖,并基于原始模型DICE+BCE损失函数,构建新型的超参数权重损失来优化网络模型中参数迭代的误差,改善道路分割的精度。按照1∶1、2∶1、3∶1、4∶1、5∶1这5种比值设定超参数权重比,通过调节超参数权值比获取模型最佳的道路分割性能。实验结果表明,与FCN-8s、U-Net等模型相比,改进D-LinkNet模型道路分割效果明显提升,能有效地规避因非道路因素对道路提取干扰而导致的“虚检”“漏检”“误检”的现象。The road information in high-resolution images tend to be disturbed by non-road information such as vegetation shadows,tall buildings and rivers.To address the problem,an improved model is proposed to extract road parts from high-resolution remote sensing images.For the construction of the model,a channel-spatial bi-attention mechanism is introduced to capture the global characteristic dependence of road information in the central region.Then the new hyperparameter weight loss is constructed based on the DICE+BCE loss of the original model to reduce the error of the parameter iteration in the network model and improve the accuracy of road segmentation.The hyperparameter weight ratio is successively set to 1:1,2:1,3:1,4:1 and 5:1,and the best road segmentation performance of the model is obtained based on the adjustment of the hyperparameter weight ratio.The experimental results show that compared with FCN-8s,U-Net and other models,the improved D-LinkNet model delivers a significant improvement in road segmentation effect.The algorithm can effectively avoid false detection and missed detection that are caused by interference of non-road factors in road extraction.

关 键 词：高分遥感影像 双注意力机制 全局特征依赖 超参数权重损失 道路分割 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]