基于深度学习的海岸线边缘检测网络模型  

A coastline edge detection network based on deep learning

作　　者：李忠瑞 崔宾阁 杨光 张昊卿 LI Zhong-rui;CUI Bin-ge;YANG Guang;ZHANG Hao-qing(School of Computer Science and Engineering,Shandong University of Science and Technology,Qingdao 266590,China)

机构地区：[1]山东科技大学计算机科学与工程学院,山东青岛266590

出　　处：《计算机工程与科学》2022年第12期2220-2229,共10页Computer Engineering & Science

基　　金：国家自然科学基金(42076189)。

摘　　要：海岸线的动态监测对海岸带的规划管理具有非常重要的意义。由于海陆环境错综复杂,遥感影像中海陆边界光谱特征不明显,导致提取的海岸线定位不准确。提出一种融合语义分割网络和边缘检测网络的深度卷积神经网络模型(EWNet)。该模型包含2个分支流:语义分割流负责提取分层语义信息并用来指导边缘检测流获取岸线语义信息;边缘检测流通过语义分割流完善边缘语义信息。在“高分一号”遥感图像上的实验结果表明,与几种最新网络模型相比,EWNet获得了更精确的海岸线边界提取结果。The dynamic monitoring of coastline is of great significance to the planning and management of coastal zone.Due to the complex sea and land environment,the spectral characteristics of the sea and land boundary in remote sensing images are not obvious,which leads to inaccurate positioning of the extracted coastline.This paper proposes a deep convolutional neural network(EWNet)combining semantic segmentation network and edge detection network.The network contains two branch streams.The semantic segmentation stream is responsible for extracting hierarchical semantic information and is used to guide the edge detection stream to obtain coastline semantic information.The edge detection stream uses the semantic segmentation stream to refine the edge semantic information.Experimental results on GF-1 remote sensing images show that,compared with several latest models,EWNet obtains more accurate coastline boundary extraction results.

关 键 词：海岸线提取 神经网络 语义分割 边缘检测 遥感图像 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]